A Comparative ROI Analysis of Leading Security Operations Platforms (2025)

Unlocking Value: A Comparative ROI Analysis of Leading Security Operations Platforms (2025)
In today's accelerating threat landscape, Security Operations Centres (SOCs) face escalating pressures to enhance efficiency, speed, and resilience. Achieving a strong Return on Investment (ROI) from SOC platform deployments is no longer just beneficial, it's imperative. This definitive study cuts through the complexity, meticulously comparing 17 leading and emerging platforms, including industry powerhouses like Google Chronicle, AWS (AI SOC approach), Microsoft Sentinel, Splunk, Palo Alto Cortex XSIAM, and emerging "AI SOC" innovators such as Anvilogic, Prophet, AISOC, Intezer, Dropzone, Qevlar. We provide critical insights tailored for both end-user enterprises and Managed Security Service Providers (MSSPs).
Our analysis is built upon four pivotal ROI criteria: (1) Analyst productivity (automation of triage, correlation, and response capabilities), (2) Detection and response speed (measurable improvements in MTTD/MTTR and real-time analytics), (3) Licensing and infrastructure costs (total cost of ownership and pricing model efficiencies), and (4) Time-to-value (onboarding speed, out-of-box content, and integration ease). This report equips you with a structured analysis, featuring real-world examples and the latest benchmarks (2024–2025), empowering you to make informed strategic decisions that maximise cybersecurity investments.
Table of Contents
Introduction & Framework
SOC Evolution Pyramid
The SIEM & XDR Titans
Traditional SIEM & XDR Platforms
Google Chronicle (Google Cloud Security Operations)
AWS "AI SOC" (Native AWS SecOps Approach)
Microsoft Sentinel
Splunk Enterprise Security
Palo Alto Cortex XSIAM
Panther, Sekoia.io & Exabeam Fusion
SentinelOne Singularity Platform (XDR + AI SIEM)
AISOC Overview
Understanding AI SOC Platforms
Next-Generation AI SOC Platforms
Prophet Security & Anvilogic
Intezer Autonomous SOC
AISOC Autonomous SOC
Qevlar AI
Dropzone AI 
Conclusions
ROI Summary: Key Findings Across Platforms
AI SOC Mesh - Example
Contact Cyber3D
Introducing the SOC Evolution Pyramid
The SOC Evolution Pyramid represents the key elements that power an effective SOC. Data being the foundational element that Humans & Machines need to enable confident decisions to take action, and at the apex, the business goals that drive the technology strategy, people, operating procedures. The SOC charter defines the protective focus and how this is achieved, by whom, with a defined governance structure that is signed off by the board.
Our ROI analysis covers the Technology Stack and Outcomes layer.
The SIEM & XDR Titans
The ROI study will begin with a purview of the current SIEM & XDR Titans. These platforms are either evolving toward some manifestation of platformization, or opening up their blackboxes to allow for decoupling of typical SIEM functions; for example storage and data pipeline management, even analytics. Whilst there are some built-in AI functions, currently they are acting more as analyst co-pilots rather than autonomous agents.
Platformization in the SIEM context represents a strategic shift from monolithic, self-contained security information and event management systems to integrated, modular security ecosystems. This involves consolidating diverse security capabilities, from traditional log management and threat detection to incident response and vulnerability management, into a unified, extensible environment. This approach fosters a more holistic view of the security posture, breaking down data silos and enabling centralised data correlation and analysis across an organisation's entire digital footprint.
The "opening of blackboxes" signifies a move towards greater transparency and interoperability. This includes offering robust APIs for custom integrations, supporting open standards like STIX/TAXII for threat intelligence sharing, providing custom parsers for ingesting diverse data sources, and adopting modular architectures. These changes allow enterprises to seamlessly integrate their preferred third-party tools, such as Security Orchestration, Automation, and Response (SOAR) platforms, Endpoint Detection and Response (EDR) solutions, or specialised threat intelligence feeds, enhancing overall security efficacy.
The **decoupling of SIEM functions**, such as separating data storage from real-time analytics or pipeline management, offers significant benefits. This modularity allows organisations to optimise costs by leveraging tiered storage solutions (e.g., cold storage for compliance data), enhance scalability, and tailor their security infrastructure by using best-of-breed tools for specific tasks. This flexibility ultimately leads to improved system performance, reduced operational overhead, and greater adaptability to evolving security needs.
Currently, **AI functions** within these platforms primarily serve as **analyst co-pilots**. They assist human analysts by automating mundane tasks, correlating alerts, flagging anomalies, suggesting threat hunting queries, and proposing automated response playbooks. This contrasts with fully **autonomous agents**, which would independently detect, analyse, and remediate threats without human intervention. While co-pilots significantly enhance efficiency and reduce alert fatigue, the industry is still some way from deploying fully autonomous security agents for complex decision-making.
This evolution is driven by several **market trends**: the exponential growth in data volume and velocity, the increasing sophistication and complexity of cyber threats, a persistent global cybersecurity talent shortage, and the imperative for organisations to achieve faster, more intelligent security responses.
Google Chronicle (Google Cloud Security Operations)
Analyst Productivity
Google Chronicle (now part of Google Cloud Security Operations) emphasises automation and high-quality threat intelligence. An IDC study found Chronicle helped SecOps teams become 42% more efficient, as analysts spent less time on low-level monitoring and more on actual threat analysis. Chronicle's integration of Mandiant intelligence and its SOAR capabilities (via Siemplify) further streamline triage and incident handling.
Detection & Response Speed
Chronicle's cloud-native analytics deliver real-time threat detection at scale. Organisations report 60% fewer security incidents after moving to Chronicle, thanks to improved detection and resolution capabilities. Chronicle's ability to ingest and search massive data (petabyte-scale) quickly helps reduce mean time to detect/respond.
Licensing & Infrastructure Costs
A major ROI driver for Chronicle is its unique pricing model decoupled from data volume. IDC found Chronicle users ingested 283% more data and 85% more log sources with no incremental cost penalty. Instead of paying by GB, customers pay a fixed fee, eliminating the cost barrier to logging "everything." This led one manufacturer to do 10× more security work at a fraction of the cost compared to their previous SIEM. Over three years, Chronicle delivered an estimated 407% ROI with payback in under 7 months, largely by reducing infrastructure costs and avoiding the expensive scaling of on-prem SIEMs. For MSSPs, Chronicle's multi-tenant-friendly SaaS model and flat pricing can translate into predictable costs even as client data volumes grow.
Time-to-Value
Chronicle is designed for rapid onboarding and scalability. Its cloud deployment and unlimited scalability mean organisations can start ingesting data and getting insights within days, not months. Google's unified security platform and out-of-the-box detections (including Gemini for SOC features) reduce the time needed to build content. Customers highlight quick ramp-ups, e.g. achieving full data ingestion parity in 2–4 months vs. 5 years on a legacy SIEM. This fast deployment (93% reduction in setup time) saved $618K in labour over three years for the composite organisation in a Forrester study. For MSSPs, Chronicle's multi-tenant SaaS and Google's support allow new client environments to be onboarded quickly with minimal infrastructure, accelerating the provider's time-to-value per client.
AWS "AI SOC" (Native AWS SecOps Approach)
Analyst Productivity
AWS does not offer a single monolithic SecOps platform; instead, it provides a toolkit of cloud-native security services (Amazon GuardDuty, Security Hub, Detective, CloudTrail & Security Lake, etc.) and AI services (Bedrock, SageMaker) that can be assembled into an "AI-powered SOC." In practice, MSSPs and enterprises using AWS benefit from automation by stitching together these services. For example, AWS Security Hub can automatically aggregate findings from various AWS services, and solutions like Sumo Logic's Dojo AI (deployed on AWS Bedrock) use "agentic" AI to automate routine investigations. Early adopters of such AI-on-AWS approaches report measurable productivity gains including reducing manual query writing and handoffs, and focusing analysts on high-value tasks.
Detection & Response Speed
AWS's security services operate in real-time within the cloud infrastructure, often detecting issues (e.g. unusual logins, malware in S3, etc.) in minutes. By leveraging cloud-scale data and AI, organisations can improve MTTD/MTTR. For instance, Sumo Logic's Dojo AI on AWS has reduced MTTR and improved accuracy by ~20% for early customers through automated incident scoping and summaries. AWS's emphasis on autonomous remediation (using Lambda functions or Systems Manager automation in response to GuardDuty findings) can drastically cut response times, sometimes neutralising threats instantly (e.g. auto-isolating compromised instances). In an MSSP context, AWS's multi-account tools (like cross-account CloudWatch metrics and central logging in Security Lake) enable providers to detect threats across client environments quickly, though integration of non-AWS data may require extra effort. AWS is also exploring generative AI for investigations (e.g. Amazon Detective + Bedrock for natural language incident analysis), which promises to speed up investigation timelines for analysts.
Licensing & Infrastructure Costs
AWS's pay-as-you-go pricing can be a double-edged sword for ROI. End-users already in AWS often benefit from no upfront infrastructure costs as all security tools are SaaS and usage-based. For example, Amazon GuardDuty charges per million events analysed, and Security Lake uses low-cost S3 storage for logs. This granular pricing means you pay only for what you use, avoiding large fixed licences. An enterprise can enable built-in AWS security for a fraction of what a third-party SIEM might charge for the same log volume. However, costs can grow with scale: high log volumes or multi-account setups may incur significant fees (e.g. storing all CloudTrail and VPC flow logs). Compared to traditional SIEM, AWS-native solutions can be cost-effective, especially if one leverages existing AWS spend commitments. MSSPs can find AWS cost models challenging if managing dozens of client accounts; however, AWS Marketplace solutions allow MSSPs to procure multi-tenant SOC platforms (e.g. SOC-as-a-Service offerings) on a subscription basis. Overall, AWS's model offers infrastructure savings (no on-prem hardware or Splunk-like indexing costs) and a flexible TCO but careful management is needed to prevent unpredictable costs.
Time-to-Value
For an AWS-centric organisation, turning on services like GuardDuty, IAM Access Analyser, and Security Hub can yield immediate security value (within hours), since these services start analysing data as soon as they're enabled. There is virtually zero deployment time, no servers to install, so initial setup is fast. The challenge is that AWS outputs many raw alerts; achieving full SOC value may require integration and tuning. This is where solutions like AI-SOC agents on AWS help tie things together. For example, an MSSP can deploy an AWS CloudFormation stack that sets up a Security Lake and an AI analysis layer in days. AWS also facilitates quick integration of third-party SOC tools (through its AI Model Context Interface and other integration points) meaning an organisation can plug in an AI assistant or SIEM alternative relatively easily. In summary, AWS offers fast time-to-enable for individual services, but time-to-value for a cohesive SOC may depend on assembly: savvy teams or partners can achieve rapid value by using automation and pre-built frameworks, whereas less experienced teams might face a learning curve connecting all the pieces.
Microsoft Sentinel
Analyst Productivity
Microsoft Sentinel (cloud-native SIEM+SOAR) has strong automation and AI-driven correlation that boost analyst efficiency. Forrester's TEI study found Sentinel improved SOC efficiency by $1.5M over 3 years by simplifying detection and investigations. Sentinel's built-in AI correlation (Fusion) and automation rules reduced tedious tasks, e.g. organisations saw 79% fewer false positives and an 85% reduction in effort for complex multi-step investigations. Junior analysts can handle more incidents via Sentinel's intuitive interface, freeing senior analysts for higher-priority work. Sentinel also integrates with Microsoft's SOAR playbooks (Logic Apps) for automated response. This process automation let some teams redeploy 50% of their infrastructure personnel and 16% of SIEM specialists to other tasks, since Sentinel's cloud management is largely hands-off. For MSSPs, Sentinel supports multi-tenant operations via Azure Lighthouse and scalable workspaces, enabling providers to manage many customers efficiently from one pane, a reason many MSSPs have adopted Sentinel.
Detection & Response Speed
Because Sentinel tightly integrates with Microsoft's security stack (Defender suite, Entra ID, etc.), it delivers fast threat detection and end-to-end response. Interviewed users cite Sentinel's "proactive, predictive abilities" to respond to threats faster than a human. The platform's analytics helped some organisations stop attacks that hit others, indicating improved MTTD. Sentinel also decreased incident response times significantly: one financial organisation ingested 6 TB/day within two months and immediately benefited from real-time analytics. By correlating signals across email, identity, endpoint, and cloud, Sentinel can catch lateral movements quickly. For example, its entity behaviour analytics can automatically link an unusual login to related alerts, accelerating triage. Customers report MTTR reductions and faster incident closures due to features like built-in incident timelines and AI-driven investigation graphs. Also notable, Sentinel's automation capabilities cut response effort. Forrester found advanced threat response work was 85% less with Sentinel in place. In MSSP scenarios, Sentinel's scalable cloud performance means providers can run analytics across large client datasets without performance lag, maintaining speedy detection across tenants.
Licencing & Infrastructure Costs
Sentinel is a cloud SaaS with a consumption-based pricing model. It charges per GB of data ingested (with predictable rates and tiered volume discounts) and includes free ingestion for many Microsoft 365 logs (Office 365, Entra ID events). This can drastically lower costs for Microsoft-centric organisations. A Forrester composite organisation saved $5.1M over three years by retiring a legacy on-prem SIEM and switching to Sentinel. Sentinel's auto-scaling cloud infrastructure means no capital expense for servers or storage, all included in the data fee. For MSSPs, Sentinel's CSP licencing or pay-as-you-go model allows aligning costs with customer contracts, and multi-tenant support avoids deploying separate infrastructure per client. The overall TCO reduction (~44% lower) and high ROI (234% ROI over 3 years per Forrester) are major advantages of Sentinel.
Time-to-Value
Microsoft Sentinel is known for fast onboarding thanks to many prebuilt connectors, parsers, and detection rules. Forrester found connecting new data sources was 93% faster than with a legacy SIEM. A cited customer ingested the same volume of data in 2–4 months on Sentinel that took 5 years on their old solution. Sentinel comes with hundreds of out-of-box analytics rules (especially if one uses Microsoft's content hub and GitHub community queries), so organisations can start catching common threats immediately. Its integration with Microsoft 365 Defender means that if an organisation already has Defender alerts, connecting Sentinel provides instant SOC visibility. Also, Sentinel's familiar Azure-based interface and KQL query language accelerate adoption (admins with Azure skills pick it up quickly). MSSPs benefit from templates and automation as they can templatise onboarding for new clients (reusing playbooks, analytics rules), often standing up a new client environment in hours. Overall, Sentinel's combination of cloud deployment (no setup delay), prebuilt content, and easy integrations yields a short time-to-value, often a few weeks to see meaningful detections, versus months with traditional SIEMs.
Splunk Enterprise Security
Analyst Productivity
Splunk ES has long been a gold-standard SIEM for flexible analytics and a rich ecosystem. If fully utilised, Splunk can significantly augment SOC productivity by centralising diverse data and automating correlations via its Enterprise Security (ES) content. Users report that after adopting Splunk ES, incident remediation took only ~25% of the time it took with their previous solution, implying analysts could resolve incidents up to 4× faster due to Splunk's powerful search and correlation abilities. Splunk's Adaptive Response framework and Phantom SOAR (now Splunk SOAR) integration enable automated enrichment and response actions, further reducing manual workloads. However, achieving these gains requires skilled tuning; some organisations face a learning curve with Splunk's SPL query language and content development. Once mastered, the platform's versatility (dashboards, custom alerts) can streamline investigations (analysts can pivot and search data in one place instead of across tools). Splunk's recent AI enhancements (like federated search with machine learning) are bolted-on, but can help suggest queries or highlight anomalies. For MSSPs, Splunk's productivity impact is mixed: it's powerful for experienced analysts who can leverage its query capabilities across multiple tenants, but the lack of native multi-tenancy means MSSPs often maintain separate indexes or instances per client, adding some overhead.
Detection & Response Speed
Splunk ES excels at real-time correlation (via its Correlation Search engine) and has a library of use-case-specific detections. Many SOCs have caught incidents they'd otherwise miss by using Splunk's behavioural analytics and threat intel integrations. With proper resource allocation, searches and alerts in Splunk can fire in near real-time for critical events. In practice, Splunk has helped organisations cut MTTD/MTTR by enabling faster pivoting through data: one user noted investigations that used to take 4–6 hours of combing logs now take minutes in Splunk because Splunk can pull an entire incident timeline with a few queries. Splunk's User Entity Behaviour Analytics (UEBA) module (premium add-on) can algorithmically detect subtle anomalies, potentially catching insider threats or lateral movement faster than rule-based systems. Response speed is augmented by Splunk SOAR, which can trigger containment (disabling accounts, isolating endpoints) automatically upon certain alerts. The net effect is often a shorter dwell time when an attack occurs. Splunk's customers often cite the platform's ability to "see across all systems in one place" for quick triage as a key advantage. That said, if not tuned, Splunk can overwhelm analysts with alerts (high volume logs generate many notable events), so ROI depends on effective filtering. MSSPs leveraging Splunk often build custom monitoring content per client to ensure high fidelity alerts, enabling them to respond quickly despite managing many environments.
Licensing & Infrastructure Costs
Cost is a well-known challenge for Splunk. Its traditional licensing is based on daily data ingestion (e.g. $1,800 per year per 1 GB/day ingest), which can become prohibitively expensive at scale. Organisations with high log volumes often ingest only a subset to control costs, potentially missing some telemetry (a hidden ROI loss in risk). Splunk's infrastructure (if self-hosted) also incurs hardware and admin costs. Some mid-size enterprises and MSSPs find Splunk's cost "difficult to justify for the ROI" unless they fully leverage its advanced features. On the other hand, when fully utilised, Splunk can deliver significant ROI by preventing costly incidents. Competitors often highlight Splunk's high TCO: for example, Google Chronicle's flat pricing allowed 283% more data ingestion at a fraction of Splunk's cost. Anvilogic estimates that offloading logs to a data lake can cut 81–87% of Splunk-related costs. Thus, Splunk's ROI on the cost dimension is mixed. Very high spend but also potentially high value if that spend is used to its fullest. Many large enterprises accept the cost due to Splunk's capabilities, while MSSPs often seek volume discounts or use Splunk for only critical log sources to manage expenses.
Time-to-Value
Splunk's flexibility means initial setup can be complex. Deploying Splunk ES on-premises might take weeks or months of planning (sizing infrastructure, installing forwarders, etc.). However, Splunk Cloud has eased deployment for many, provisioning an instance is relatively quick, and numerous out-of-box use-case apps (for Windows, AWS, Palo Alto, etc.) provide prebuilt dashboards and rules that shorten time-to-value. For example, Splunk ES comes with a correlation search library and risk-based alerting framework ready to use. In practice, organisations typically spend a few months tuning Splunk to their environment (building indexes, data onboarding, custom dashboards). Onboarding time for an MSSP can be significant: each new client might require parsing new log formats unless using standardised data onboarders. On the plus side, Splunk's huge community and content (via Splunkbase) mean new detections or integrations can be added quickly by importing existing content packs. In summary, Splunk's time-to-value is moderate, not as immediate as turnkey SaaS solutions, but once deployed, its rich content and user familiarity (Splunk has been around for over a decade) often lead to steady improvements in detection capability over time. Enterprises that invest in training analysts on Splunk likely see faster value realisation (since skilled use is key to ROI).
Palo Alto Cortex XSIAM
Analyst Productivity
Cortex XSIAM is Palo Alto's AI-driven SOC platform converging SIEM, XDR, and SOAR. By unifying these functions, XSIAM reduces the "swivel-chair" effect for analysts, less context switching between tools. In deployments like Orange Cyberdefence's MDR service, XSIAM's automation eliminated many manual steps: it automatically correlates endpoint, network, identity, and cloud data into entity-centric alerts, which cuts down analyst triage time. Palo Alto reports that XSIAM's design removes investigation bottlenecks by providing a complete incident story across data sources. However, XSIAM's current AI automation still relies on predefined rules/playbooks rather than self-learning, meaning some manual tuning is needed. Once set up, customers have noted improved efficiency. Overall, XSIAM can significantly boost productivity by consolidating tools (one console for SOC), though the greatest gains come when organisations fully adopt Palo Alto's ecosystem.
Detection & Response Speed
XSIAM leverages machine learning and Palo Alto's extensive threat intel to detect attacks across network and endpoint vectors in real time. Its unified data model means it can identify multi-stage attacks faster e.g. correlating a firewall alert with an endpoint behaviour without human intervention. Users have reported substantially faster detection of stealthy threats: integrating network telemetry increased detection of confirmed incidents by 138% in one case, catching threats that siloed tools missed. By having built-in SOAR, XSIAM also accelerates response as it can automatically trigger containment via Cortex XDR agents or firewall policies. With XSIAM 3.0 (2025 update), Palo Alto even introduced generative AI features to further speed up incident investigation. The flip side is that XSIAM's effectiveness depends on integrating all relevant data; partial adoption yields partial improvement. In an MSSP setting, XSIAM's new multi-tenant architecture (child tenants managed under one MSSP account) ensures providers can investigate and respond across clients quickly. Overall, when fully utilised, XSIAM delivers rapid threat suppression.
Licensing & Infrastructure Costs
XSIAM is a cloud-delivered platform (on GCP) offered on a per-endpoint or capacity subscription. It is generally positioned as a premium solution, likely costlier than standalone SIEM or XDR, since it replaces multiple tools. The ROI case for XSIAM often hinges on consolidation savings: instead of paying for a SIEM + an EDR + a SOAR separately, XSIAM provides all-in-one. Palo Alto claims this consolidation results in a more predictable TCO. Orange Cyberdefence highlighted that using XSIAM let them reduce the overall solution stack, achieving a more predictable and lower total cost of ownership for their service. By offloading data to a built-in data lake and using automation, organisations can save on legacy SIEM storage costs and labour. Still, absolute licensing costs for XSIAM can be high because it's targeted at large enterprises and MSSPs with sizable budgets. Some considerations: if an organisation already has investments in Palo Alto networks and endpoints, XSIAM leverages those (potentially offsetting its cost by improving their ROI). For MSSPs, Palo Alto introduced MSSP-specific licensing to allow managing multiple client environments economically. While exact figures aren't public, MSSPs can create "child tenants" on demand, suggesting a usage-based model that scales. In summary, XSIAM's cost ROI depends on environment size and tool consolidation, it avoids the "many point product" costs, but requires a significant investment in the Palo Alto stack. When it prevents a major breach or allows a leaner team to cover the same work, the ROI is favourable.
Time-to-Value
XSIAM deployments require careful planning. It's not a plug-and-play tool; migrating from existing systems and integrating diverse data feeds can take time (and often Palo Alto professional services). Palo Alto's own guidance and early adopters stress that implementing XSIAM effectively demands significant expertise and can be complex if you have many legacy tools. An enterprise might spend several months on the initial rollout (data onboarding, rule tuning). However, once up, the platform delivers quick value by immediately correlating signals that were previously siloed. The platform includes hundreds of integrations and detection rules for common log sources, which Orange says "ultimately leads to faster time to value" by reducing complexity in onboarding. For MSSPs, Palo Alto's multi-tenant enhancements and the ability to templatise onboardings (plus re-use detection content across tenants) shorten the time-to-value for each new client. In short, XSIAM has a slower initial deployment curve than simpler SIEMs or point solutions, but for committed adopters, it pays off rapidly once running by consolidating capabilities which leads to immediate improvements in visibility and incident handling.
Panther, Sekoia.io & Exabeam Fusion
Panther
Analyst Productivity
Panther is a modern cloud-native SIEM that uses a detection-as-code approach (Python rules) and serverless architecture. It focuses on reducing toil by eliminating infrastructure management (no servers or indexers) and providing an intuitive UI plus powerful customisation. Users praise Panther's convenient, intuitive interface and 24/7 support, noting that even without deep Python knowledge their team ramped up quickly. This ease of use translates to analysts spending more time on writing detection logic or investigating alerts rather than maintaining the SIEM. Panther's real-time alert pipeline and flexible schema on read means less time normalising data upfront. Productivity is also enhanced through Panther's built-in rules and community Sigma rules, analysts can import or adapt these rather than coding from scratch. Compared to older SIEMs, Panther's lack of query language complexity (it uses Python and JSON for rules vs. proprietary SPL or KQL) can flatten the learning curve. That said, because Panther encourages "as-code" workflows, security engineers who are comfortable with code will maximise its benefits. In smaller teams without a dedicated engineer, there may be some initial effort to learn the environment. For MSSPs, Panther's multi-tenant story is evolving as it doesn't natively separate tenants, but MSSPs can deploy separate Panther instances or segregate data in a single data lake. Some MSSPs have begun using Panther to leverage its cost efficiency and fast search for client monitoring, which allows their analysts to manage detections via code repositories (a productivity boon if integrated with CI/CD workflows for multiple customers).
Detection & Response Speed
Panther is designed for speed at scale. It ingests data into cloud data lakes (like AWS S3/Snowflake) and can run detection queries in near real-time using serverless functions. This architecture means even large volumes of log data can be analysed quickly without the query slowdowns that can plague on-prem SIEMs under load. In practice, Panther can detect threats within seconds to a couple minutes of ingestion, depending on rule schedule. Its real strength is in scalability, where other SIEMs might delay or drop alerts due to throughput limits, Panther scales up transparently. Also, Panther's use of Python for detection logic enables more complex analytic logic (accounting for context in code) which can improve detection accuracy and reduce noise. While we don't have public MTTD metrics, anecdotal reports indicate that organisations have high confidence in Panther catching issues promptly, provided they have written or enabled the relevant detection rules. For incident response, Panther integrates with SOAR tools (and AWS Lambda) to trigger responses, though it doesn't have a built-in SOAR of its own. Still, by integrating with services like PagerDuty, Slack, or Jira, it ensures the team is notified immediately, helping reduce MTTR. One area Panther shines is cloud threat detection (AWS, GCP, etc.): it provides many out-of-the-box policies for cloud misconfigurations and suspicious activities, leading to quick wins in detecting issues that might go unnoticed. Overall, if properly tuned, Panther offers detection speed on par with the fastest solutions, and can give a lean team quick reaction capability without performance bottlenecks.
Licensing & Infrastructure Costs
Cost efficiency is a key selling point for Panther. Unlike Splunk's volume-based licencing, Panther primarily charges based on data volumes stored in the cloud data warehouse plus a platform fee, often resulting in much lower costs at scale. A comparison noted Panther can provide higher ROI in the long run due to cost-efficiency especially when log volumes are high, whereas Splunk's costs "scale rapidly" with data volume. Panther leverages the relatively cheap storage of cloud data lakes (e.g. Snowflake, which has separate compute/storage) so customers aren't paying a premium for keeping historical logs. For example, 500 GB/day of logs retained for a year might cost on the order of $100K on Snowflake, whereas traditional SIEMs could charge many times that (Splunk or Sumo could be $500K+). Panther's serverless model also means no paying for idle compute as you pay only when processing queries or alerts. All this translates to a significantly lower TCO for the same or greater amount of data, giving Panther one of the best cost-to-ingested-data ratios in the industry. For ROI, that means organisations can afford to ingest all relevant data, improving security outcomes without blowing the budget. Panther does have a SaaS subscription cost, but it's generally flat or based on usage tiers and considered reasonable. For MSSPs, Panther's cost model is attractive since they can pool client data into one platform (if compliance permits) and not incur separate licence fees per client, just incremental storage/compute costs. This makes it possible for MSSPs to offer SIEM-as-a-service at a lower price point to customers, while still maintaining margin. Summed up, Panther often demonstrates 80%+ cost savings vs legacy SIEMs for large data volumes (community anecdotes and competitive analyses back this), directly boosting ROI.
Time-to-Value
Panther being SaaS means deployment is quick, typically a cloud stack is set up within a day or two. Onboarding log sources is streamlined via a mix of agentless ingestion (e.g. AWS CloudWatch subscriptions, GCP Pub/Sub) and lightweight open-source Panther sensors. Many common log formats and cloud sources have prebuilt parsers and out-of-box detection rules (Panther comes with rule packs for AWS, GSuite, Okta, etc.). This gives organisations immediate basic coverage. Some users note that because Panther is newer, a few dashboards or connectors were still maturing, but the company has been rapidly improving these. The learning curve for writing detection-as-code is modest for those with scripting experience, but Panther also provides a UI rule builder and a library of community rules, reducing initial content development time. In one example, a company that replaced Splunk with Panther found that their security team could pivot to detection engineering in just weeks since Panther's platform required less care-and-feeding than Splunk (freeing time to write rules). For an MSSP or a fast-moving business, Panther's ability to stand up a full SIEM in days and start getting alerts as soon as data flows in is a major time-to-value advantage. The elimination of setup tasks like index management, server tuning, and manual scaling means the SOC can focus on value-generating work (detections, investigations) almost immediately. In summary, Panther provides a swift time-to-value: minimal deployment friction and useful detections out-of-box allow security teams to see results in days to a couple of weeks, much faster than legacy SIEM projects.
Sekoia.io
Analyst Productivity & Detection Speed
Sekoia is a European "AI-SOC" platform that combines SIEM, threat intelligence (CTI), and SOAR. It's designed to boost SOC team productivity by enriching alerts with contextual threat intel and automating workflows. Users on Gartner Peer Insights highlight Sekoia's intuitive interface and advanced analytics which "significantly enhanced our alert triage process", allowing analysts to make decisions faster. The platform's integrated CTI feeds save analysts time by automatically correlating IOCs (indicators of compromise) with alerts. One MSSP SOC lead stated, "Sekoia's integrated CTI impressed us from the start," and noted that using Sekoia elevated their clients' security while showcasing the MSSP's own expertise. This implies analysts could handle more and communicate value better, rather than being bogged down in noise. Sekoia also offers a library of detection rules (covering MITRE ATT&CK use cases) out-of-the-box, reducing the burden on analysts to create content. These rules and automated playbooks (for enrichment or response) reduce manual tasks. Overall, Sekoia's focus on context (through threat intel) and workflow integration leads to analysts spending less time gathering information and more time on true investigative work, improving productivity and morale (less burnout).
Sekoia's SOC platform has real-time detection capabilities and is tuned to catch sophisticated threats quickly, aided by its curated intel. This underscores faster threat detection compared to a legacy setup. Additionally, a major French enterprise said that in just 3 weeks of using Sekoia, they achieved a higher level of detection than with their previous solution. Such a rapid improvement speaks to Sekoia's strong default rules and analytics (likely mapping to many ATT&CK techniques out-of-box). On response, Sekoia includes SOAR functions enabling automated containment or notification. Its platform can trigger responses or create tickets when high fidelity alerts fire, shortening the MTTR. For example, if an endpoint malware alert comes in, Sekoia can automatically enrich it with threat intel (is the hash known malicious?) and even isolate the host via an EDR integration, all steps that would otherwise take an analyst many minutes. As an MSSP-enabling platform, Sekoia allows providers to respond across multiple client environments efficiently. The bottom line: Sekoia improves detection coverage (catching more, sooner) and shaves response times through automation, directly improving ROI by minimising potential damage from incidents.
Licensing & Infrastructure Costs
Sekoia positions itself as a cost-effective alternative to big-name SIEMs, which is validated by customer statements. A SOC manager review explicitly said "Their prices are very competitive compared to their competitors" and praised Sekoia's responsiveness to customer needs. As a SaaS platform hosted in the cloud (with data region options for European customers), Sekoia avoids on-prem infra costs. Licencing is typically subscription-based, scaled by data volume or EPS (events per second) tiers, but generally priced lower than Splunk or QRadar for similar volumes. The ROI here is that organisations can afford to ingest broader data (their full logs) into Sekoia without the severe cost constraints of legacy SIEMs. Also, by bundling CTI and SOAR, Sekoia can replace multiple products with one, simplifying the stack and potentially saving cost. An example ROI factor is reduced breach cost: by catching incidents faster, Sekoia can prevent costly incidents. For MSSPs, Sekoia offers a partner programme and multi-tenant support, likely with volume-based pricing that allows the MSSP to profitably serve smaller clients. Since the platform is all-in-one, MSSPs don't need to licence a separate threat intel feed or SOAR tool, further saving on overall solution cost. In summary, Sekoia provides strong cost-to-value: competitive SaaS pricing, consolidation of capabilities, and prevention of incidents all contribute to high ROI.
Time-to-Value
Sekoia is designed for quick deployment and immediate effectiveness. As a SaaS, deployment is mostly about feeding it data. Connectors for common log sources (Windows, O365, firewall, EDR, etc.) are available, and Sekoia's team often assists new customers (or MSSPs) in onboarding within days. Factors contributing to this include its rich set of built-in detection rules and threat intel: users don't start from scratch, the system is already primed with known threats and behaviours. Sekoia's intuitive UI and good documentation mean security teams can learn it quickly (fast skill acquisition was mentioned in reviews). Its cloud model also means scaling or adding new data sources is straightforward. So, whether it's an enterprise replacing an old SIEM or an MSSP adding Sekoia to their stack, the time-to-value is short and often measurable in a few weeks to reach full SOC visibility and benefit. This swift ROI realisation is a strong point for Sekoia, especially for organisations who need to plug gaps quickly (e.g., if they had no prior SIEM or an underperforming one).
Exabeam Fusion (Next-Gen SIEM/UEBA)
01
Analyst Productivity
Exabeam Fusion SIEM is known for its user behaviour analytics (UEBA) and Smart Timeline capabilities that dramatically cut down analyst investigation effort. By automatically creating incident timelines and risk scores, Exabeam relieves analysts from manually correlating logs. In fact, a Forrester TEI study found that before Exabeam, a typical incident investigation took 6 hours (360 minutes), but after deploying Exabeam it only took 5 minutes to find the issue and determine response, essentially a 98% reduction in investigation time. This is a massive productivity boost: tasks that used to consume entire analyst shifts are done in moments by Exabeam's AI-driven correlation. Additionally, Exabeam's Incident Responder (SOAR) can automate routine responses, and its Insider Threat detection reduces time spent chasing false leads. For MSSPs, Exabeam's multi-tenant SaaS and risk-based alerts allow one analyst to handle many clients' alerts efficiently, focusing only on high-risk scored incidents across the board.
02
Detection & Response Speed
Exabeam's strength is speedy detection of complex threats (especially insider threats or lateral movement) through behaviour baselining. It provides greater visibility into "normal vs abnormal" user and entity behaviour, which means faster detection of things like account compromise that traditional rule-based systems might miss. This insider focus closes a gap as organisations realised they weren't watching internal threats until Exabeam gave them that insight. With risk scoring continuously calculated, an Exabeam deployment often surfaces an incident as soon as the risk score crosses a threshold, rather than waiting for a static rule to fire, effectively catching incidents earlier in the kill chain. Response is accelerated via the Fusion of SIEM+SOAR: Exabeam can automatically trigger actions when high-risk events occur (disable user, isolate host, etc.), cutting down MTTR. The TEI highlights that Exabeam's centralised view "greatly reduced false positives and shortened mean time to respond and resolve" incidents. Quantitatively, Exabeam customers in the study saw mean time to respond improvements in the order of multi-hours to minutes, as noted above. Another tangible metric: Exabeam enabled an organisation's SOC to reduce their backlog of alerts drastically (75% fewer incidents requiring investigation means they can respond to the remaining ones much faster). Therefore, Exabeam clearly delivers on detection speed (especially for subtle threats) and response acceleration (through automated investigation and playbooks).
03
Licensing & Infrastructure Costs
Exabeam's licencing historically is not based on log volume, but on number of users or assets monitored (or tiered by size of environment). This usage model often yields cost savings in log-heavy environments because you're not penalised for ingesting more data. The Forrester TEI study quantified Exabeam's financial impact: a composite customer achieved 245% ROI over three years, with $3.73M in benefits. These benefits include both direct cost savings (like retiring legacy SIEM hardware/software) and productivity gains, but notably Exabeam being cloud-delivered means avoiding large infrastructure costs and administrative overhead. One cited benefit was £100K+ per year saved by moving to a cloud SIEM (Exabeam) instead of on-prem, due to not having to manage SIEM servers and storage scaling. Furthermore, Exabeam's effect on reducing incidents and breach risk can save potentially millions by preventing incidents. While Exabeam is not "cheap" (it's enterprise-grade), customers often find it cost-justified by the advanced capabilities, for example, consolidating multiple point solutions (UEBA, SIEM, incident case management) into Exabeam Fusion could eliminate other licence fees. For MSSPs, Exabeam offers special licencing and a "MSSP Multi-Tenancy Module", enabling providers to spread costs and manage clients in one UI. The ROI for MSSPs includes being able to serve more customers with the same analyst pool (thanks to automation), which is a revenue gain relative to cost. Overall, Exabeam's ROI on cost is solid: high upfront subscription cost but offset by elimination of legacy costs and improved efficiency. The TEI of 245% ROI means more than doubling return, indicating Exabeam typically pays back its investment quickly by lowering labour costs and incident costs.
04
Time-to-Value
Exabeam being a cloud service (Fusion SaaS) means deployment is faster than older on-prem SIEMs. Still, it requires connecting various log sources and fine-tuning parsing. Many organisations have seen value in the first few weeks by enabling Exabeam's UEBA as often it will flag anomalous behaviours that weren't known before as soon as it has baseline data (which might be a week or two of learning). The TEI customer interviews indicate that once Exabeam was up, the improvements were immediately noticeable: teams quickly had centralised visibility and were investigating far fewer but more meaningful incidents. Exabeam provides prebuilt content (ML models for common user behaviours, correlation rules for known attacks, etc.), so out-of-box it starts doing useful analytics. This suggests a rapid ramp-up to effective monitoring. There is some learning curve for analysts to interpret risk scores and use the interface, but Exabeam's training and the intuitive timeline views mitigate that. For MSSPs, Exabeam's time-to-value can be a selling point; e.g., BlueVoyant (an MDR provider) offers Exabeam-based services and a Forrester study showed BlueVoyant's MDR (using Exabeam) delivered 210% ROI to clients by effective detection and response. Part of that value comes from how quickly the MDR can start protecting the client with Exabeam's capabilities. In conclusion, Exabeam can deliver meaningful security outcomes in a short time frame (within the first quarter of use), far faster than legacy SIEMs that might take a year to mature, thus accelerating ROI realisation
SentinelOne Singularity Platform (XDR + AI SIEM)
Analyst Productivity
SentinelOne's Singularity XDR platform is an autonomous cybersecurity platform with endpoint, cloud, and identity protection that offloads a great deal of work from human analysts. It excels in automated detection and remediation at the endpoint, meaning analysts spend far less time on basic malware alerts or hands-on cleanup as the agent often kills and quarantines malware by itself. A Forrester study of SentinelOne found a 353% ROI over three years, heavily driven by labour saved and tools consolidated. By replacing legacy AV and other tools with one platform, coverage doubled (50% to 100% of endpoints covered) while security staff didn't need to increase. One metric: SentinelOne's automation and visibility saved organisations 300 analyst hours per month on average, by simplifying incident investigation and automating response. Analysts no longer have to manually gather forensic data or reimage machines and SentinelOne's rollback feature can reverse malicious changes in one click, and rich EDR telemetry provides root cause analysis instantly. This means the SOC can handle more incidents with the same or fewer people. For MSSPs, SentinelOne is a force multiplier, many MDR providers can use it to allow a small team to manage thousands of endpoints effectively. As one security architect put it, "I've never seen anything like it" in terms of coverage and automatic stopping of threats, thereby dramatically improving SOC efficiency.
Detection & Response Speed
Speed is a hallmark of SentinelOne. It operates on the endpoint in real time, detecting ransomware or exploits within milliseconds via behavioural AI. This immediate on-agent detection means mean time to detect is essentially near-zero for many endpoint threats (the agent doesn't need to check with a cloud service to act). The result is many attacks are stopped before they spread, yielding effectively a zero dwell time for those threats. In terms of response, SentinelOne's autonomous containment can neutralise a threat at machine speed (e.g., killing a malicious process the moment it's identified, or disconnecting an endpoint from the network). The platform's visibility across the entire enterprise (endpoint, cloud workload, and now data via its logging capabilities) also means analysts can rapidly investigate if something did happen. The Forrester study indicated SentinelOne significantly reduced the risk of successful malware/ransomware attacks, saving $670K in incident costs by preventing incidents or catching them early. It also highlighted SentinelOne's ability to resolve incidents faster, saving $1.2M in IT and security team time over 3 years. Qualitatively, users often share experiences of detecting and stopping sophisticated attacks faster than ever; for example, identifying patient-zero of a fileless malware attack in minutes using SentinelOne's console, whereas previously it might have taken days of log hunting. In summary, SentinelOne provides instantaneous detection and response on the endpoint, and its XDR capabilities are extending that speed to other domains (it can ingest and correlate alerts from cloud/identity to catch things like identity-based attacks rapidly as well). Faster containment obviously reduces damage, which is a massive ROI factor in terms of avoiding breach costs.
Licensing & Infrastructure Costs
SentinelOne is typically licensed per endpoint/agent (and additional modules for cloud, identity, etc.), which often turns out to be cost-efficient compared to a stack of separate security tools. The consolidation effect is key: organisations removed legacy AV, legacy EDR, perhaps even some SIEM costs by relying on SentinelOne's data, achieving $3M in savings through consolidation as per Forrester. There's also savings in reduced successful attacks (no ransomware payouts, less downtime). While the licence cost per endpoint might seem significant, the value is very high, e.g., SentinelOne's ransomware warranty and proven track record give confidence that costly breaches will be averted. The platform is cloud-managed, so no on-prem infrastructure cost for the console. Storage of telemetry in the Singularity Data Lake is a paid feature, but again priced more like SaaS than SIEM (and you can choose how much to retain). The TEI analysis calculated a Net Present Value of $7.9M over 3 years with SentinelOne meaning the benefits far outweighed the costs. Another cost-related ROI point: SentinelOne's automation allowed organisations to avoid hiring additional analysts even as their environment grew, which is a salary cost saving. Many firms struggle with the cost (and scarcity) of skilled security personnel; SentinelOne partially mitigates that by doing more with AI. For MSSPs, SentinelOne offers volume-based pricing and an MSSP programme, enabling them to include it as part of their service at a profit. Its multi-tenant management and flexible licensing let MSSPs scale up clients easily without huge upfront costs. Thus, SentinelOne often pays for itself by consolidating tools and preventing expensive incidents, delivering strong ROI in monetary terms.
Time-to-Value
Deploying SentinelOne is notably fast. In many cases, an organisation can roll out the lightweight agent to thousands of endpoints within days (using software deployment tools). Protection starts immediately upon agent installation, there are known stories of SentinelOne agents catching dormant malware on systems minutes after deployment, providing instant value. The platform is largely operational out-of-box: its AI models and threat databases are built-in, so you're not spending time writing detection rules for malware or common exploits. The Forrester study notes the ease of replacing legacy solutions and how SentinelOne "helped completely replace more cumbersome on-prem solutions" with a single cloud platform requiring fewer FTE hours to manage. Many organisations see meaningful improvement in their security posture within the first week or two of deploying SentinelOne, for instance, immediately stopping a malware outbreak that previous antivirus missed, or closing visibility gaps in the environment. The consolidation also means less time integrating multiple products as one platform covers many bases. In terms of user training, SentinelOne's console is fairly straightforward for basic alert monitoring, and the more advanced hunting can be learned over a few sessions (and they offer training resources). For an MSSP, onboarding a new customer with SentinelOne is quick as you provision tenant, deploy agents, and the environment is protected within days. All these factors contribute to an extremely short time-to-value, often measured in days, not months, making ROI realisation almost immediate in some cases (especially if SentinelOne thwarts a serious attack in its first month of use).
AISOC Overview
The evolving landscape of security operations has led to some confusion around the term 'AISOC'. Traditional Security Operations (SOCs) have faced significant challenges over the past decade.
As illustrated in the accompanying diagram, A SOC is where humans do work and this is where most AISOC platforms are focusing, to solve the industry challenges that have to date, not gone away.
These challenges include a critical analyst shortage leading to severe alert fatigue where analysts are overwhelmed by noisy telemetry. The impact of this is analyst disillusionment and demotivation, and therefore high attrition rates in the SOC. Studies show that over 70% of alerts are dismissed without full investigation. Additionally, a lack of specialised skills in detection engineering, SOAR automation and threat hunting has hindered SOC maturity and impacted its perceived value. Analyst inertia due to Tier 1 glass ceilings have seen high calibre talent leave the industry due to no progress in their ongoing personal development, and the cost of hiring, plus operational overhead for SOCs is mounting.
However, recent advancements in Large Language Models, Machine Learning, and Autonomous Agents have introduced a paradigm shift. Where human capital is scarce and disillusioned, AI can now provide consistent, 24/7 support, automating tasks like triage, correlation, and initial response at Tier 1. This empowers human analysts to focus on more complex threat hunting and strategic initiatives, improving productivity and reducing Mean Time To Detect (MTTD) and Mean Time To Respond (MTTR).
Crucially, this AI capability does NOT detract from the fundamental requirement for quality data for detection analytics. Quality data collection and processing remain the foundation for achieving high confidence actionable outcomes in any SOC. AI functions act as a powerful layer built upon a well-architected data ontology, amplifying its effectiveness.
"AI-SOC" Platforms 
The new breed of "AI-SOC" platforms like Prophet, Anvilogic, Intezer and AISOC specifically focus on maximising ROI via advanced automation and AI. These platforms are typically vendor-agnostic layers that sit on top of existing tools or data lakes to automate investigations, correlation, and response. 
Prophet & Anvilogic
Analyst Productivity
AI-SOC platforms act as "virtual Tier-1 and Tier-2 analysts." For example, Prophet Security's AI SOC automates alert investigations, reducing investigation time by up to 90%. This means an alert that took an analyst 1 hour is handled in minutes by the AI. Prophet customers saw this translate to hundreds of hours saved per month and $400K/year in analyst cost savings. Anvilogic similarly uses AI to automate detection engineering and triage; it can auto-discover data feeds, deploy curated detection rules, and even tune them with ML, which saves massive analyst hours that would be spent writing and maintaining rules. By applying a "detection-as-code" and AI approach, Anvilogic helps a small team manage a large detection portfolio across hybrid cloud logs. In short, these AI-SOC platforms free analysts from grunt work, e.g., Prophet's AI pulls all contextual data and even drafts an investigation summary, so the human just validates and responds. This reduces burnout and turnover, an ROI aspect often overlooked; retaining skilled staff avoids costly hiring (Prophet notes replacing a trained analyst can cost 1.5–2× their salary, so preventing burnout via AI assistance has tangible value). MSSPs adopting AI-SOC agents (like Dropzone or Blinkops) can scale their services dramatically.
Detection & Response Speed
AI-SOC platforms accelerate MTTD and MTTR by automating correlation and even performing some response actions. They excel at cross-tool correlation in seconds, something human analysts might take hours to do manually (as noted in the Dropzone AI example: manual context switching "slows everything down", whereas an AI agent can pull together identity, endpoint, cloud logs in one flow, reducing mean time to triage dramatically). Prophet Security's agents work 24/7, consistently investigating every alert across all severities so nothing slips through cracks. This leads to faster escalation of true incidents. Real-world outcomes include reducing dwell time significantly, e.g., a survey by Prophet found AI SOC adopters overcame alert overload and reduced attacker dwell times in the network. Response is also quicker because these platforms often integrate with SOAR: they might auto-isolate a machine or reset an account as part of their playbook if certain conditions are met, cutting down MTTR. The consistency of AI also means incidents get the same thorough treatment at 3 AM as at 3 PM. Many organisations find that with AI SOC, their MTTI (mean time to investigate) plummets, which in turn shortens MTTR since decisions are made faster. Overall, these platforms are delivering near real-time triage and investigation which is an enormous speed-up that directly reduces potential breach impact.
Licensing & Infrastructure Costs
ROI here often comes from tool consolidation and efficient data usage. Anvilogic, for instance, enables security teams to offload expensive SIEM storage to cheap data lakes (Snowflake, Databricks) and use AI to detect threats there, cutting SIEM licensing by up to 80–85% as shown in their calculator. By keeping only high-value alerts in the SIEM and pushing raw logs to a lake, companies maintain visibility at a fraction of the cost (e.g., $500K Splunk cost vs $93K on Snowflake with Anvilogic = ~81% savings). The AI SOC overlay itself has a cost (subscription), but is often justified by the reduction in other costs: fewer staff needed, smaller SIEM tiers, or even replacing SIEM entirely in some cases. Prophet Security emphasises a model where their AI agent sits atop existing investments, "integrate and automate your existing stack" to maximise ROI without buying more tools. That means you pay for the AI platform, but you get more value out of all the tools you've already paid for (EDR, SIEM, etc., which might have been underutilised). One MSSP case study or testimonial likely shows that the cost of the AI platform is easily offset by being able to manage more customers without linear headcount growth. In summary, AI SOC platforms can lower TCO by optimising tool usage and reducing alert-driven costs (like fines or losses from missed incidents), delivering strong ROI. For instance, Prophet's automated investigations saving $400K/year in analyst cost is a clear financial win.
Time-to-Value
These solutions are generally quick to deploy on top of an existing ecosystem. Prophet Security's AI SOC can connect to your SIEM and tools via APIs in days, and start investigating alerts immediately. In customer anecdotes, companies often see an instant drop in alert queues once the AI agent is enabled; e.g., "cut our alert queue from thousands to dozens" overnight. That indicates value is realised in the first weeks (or even days) of use. Anvilogic similarly can be layered on as its library of "Thousands of curated rules" and AI tuning means as soon as it's ingesting logs, it begins detecting and prioritising threats with minimal human-made content needed. Perhaps one of the fastest examples of time-to-value is how AI SOC agents handle ingestion and normalisation automatically. Anvilogic's platform, for example, auto-classifies and normalises incoming logs into its security graph, eliminating lengthy data mapping projects. This allows organisations (or MSSPs) to start monitoring new data sources in hours rather than weeks. The autonomy of these AI agents also means you don't need to train them extensively; they come pre-trained on cybersecurity workflows. As a result, companies often report being fully up and running with AI SOC capabilities in a month or less, and seeing improvements like reduced MTTR in that first month. The quick wins, such as immediately catching overlooked alerts or freeing analysts' time, provide tangible proof of value to stakeholders almost right away. This agility in achieving ROI is a major advantage of AI-SOC platforms.
Intezer Autonomous SOC
This AI-driven platform claims to "act as a force multiplier for security teams" by automating routine tasks and accelerating incident handling. Intezer are one of the pioneers of AI automation within the SOC and have been leading in this space for a decade. Intezer was founded in 2015 and is headquartered in New York, with R&D operations in Tel Aviv, Israel.
Analyst Productivity Gains (Tier-1 Automation & Triage)
Intezer’s Autonomous SOC uses AI to fully automate Tier-1 SOC tasks, from alert intake to evidence gathering and triage. Acting as a "virtual Tier-1 analyst," the platform dramatically reduces manual workload. Over 95% of alerts require no human intervention, as the AI filters out benign events and false positives. Only about 4% of ingested alerts are escalated as critical issues needing human review. This allows security teams to handle higher alert volumes without adding headcount.
Autonomous Triage & Correlation: The platform automatically investigates each alert, pulling in context from endpoints, identity logs, and network data, correlating related alerts for enriched analysis. This eliminates time analysts spend pivoting between tools.
False Positive Reduction: Intezer’s AI distinguishes benign anomalies from real threats, auto-resolving noise and filtering out the majority of alerts as non-malicious. This directly cuts down alert fatigue.
Minimal Escalation Load: The system escalates only high-priority incidents with clear evidence, averaging just ~4% of alerts. This significantly reduces the caseload for human teams.
24×7 Consistency: The AI SOC agent works round the clock, investigating every alert without breaks or burnout. This ensures no alert slips through the cracks, crucial for 24/7 coverage where humans often struggle to remain consistent and motivated in the small hours of Sunday morning.
These capabilities lead to massive productivity gains. Routine Tier-1 duties are handled autonomously, reducing investigation time from 30+ minutes to seconds a 60× faster investigation time. This not only saves labour costs but frees skilled staff for higher-value work like threat hunting. By removing drudgery, the platform also improves analyst morale and retention, reducing burnout and turnover.
Detection and Response Speed (Sub-Minute Detection & Automation)
Intezer’s Autonomous SOC accelerates threat detection and incident response, shrinking attacker dwell time. Unlike traditional SIEM workflows that take minutes or hours, Intezer’s AI works in real time, achieving sub-minute detection. Suspicious activity is recognised and confirmed within seconds. Parallel evidence collection (memory snapshots, file analysis, log search) allows the AI to triage an alert orders of magnitude faster than a human, directly lowering Mean Time to Detect (MTTD) and Mean Time to Respond (MTTR).
The platform doesn’t just detect faster; it jumpstarts the response. Upon confirming a serious threat, Intezer’s system generates a rich incident report with root-cause analysis and remediation recommendations. Many response actions can be automated via workflow integrations, triggering predefined actions (isolating a host, disabling an account, blocking an IP) within seconds of detection. For example, it worked with an EDR to automatically triage endpoint alerts and facilitate rapid containment of ransomware. By the time a human analyst is involved, containment might already be underway.
Parallel Evidence Collection: The AI agent swiftly gathers all related artifacts (files, process details, memory dumps, URL scans, logs) and analyses them in parallel within seconds.
Real-Time Correlation: Intezer’s AI models perform on-the-fly correlation of events across users, endpoints, and cloud workloads to surface broader attack campaigns in near-real-time.
Immediate Escalation of High-Severity Threats: The AI escalates genuine incidents (e.g., malware infection) in near-real-time with high-confidence evidence, alerting security personnel within moments of the initial alert.
Integrated Response Workflows: The platform plugs into response workflows via APIs and SOAR connectors, allowing programmatic enforcement actions for rapid containment. Fast recommendations and rich context ensure much faster remediation.
For MSSPs, this speed at scale is a competitive differentiator, enabling strict SLA commitments. Intezer’s platform delivers near-instantaneous insight and action, transforming a prolonged, manual triage process into an automated workflow where malicious incidents are addressed almost as soon as they occur.
Rapid Time-to-Value (Fast Deployment & Out-of-Box Effectiveness)
Intezer’s Autonomous SOC provides quick time-to-value, with benefits realised almost immediately. Unlike traditional tools requiring extensive tuning, Intezer’s AI SOC agent comes with pre-trained intelligence from day one, built on years of R&D and diverse threat data.
Easy Integration: The platform integrates quickly with popular data sources (SIEMs, EDRs, cloud logs, phishing inboxes) via API or out-of-box connectors. MSSPs can connect customer data feeds and start automated investigations immediately upon onboarding.
No Complex Tuning Required: Intezer’s solution doesn’t demand painstaking threshold tuning or custom rule creation per tenant. The AI agent intelligently adapts to different environments and data streams out-of-box.
Pre-Validated Accuracy: Rigorous accuracy testing ensures high fidelity results. The AI’s decision logic, honed on real-world alert data, yields strong precision and recall, reducing the "trial and error" period. Its consistent auto-resolution of benign alerts means users quickly see their alert queue shrink.
Scalable, Cloud-Native Architecture: Delivered as a cloud-based service with multi-tenant architecture, it simplifies deployment for MSSPs. Providers can spin up private instances or use a multi-tenant console, scaling to additional clients without hiring additional analysts.
Comprehensive Threat Coverage (Malware Analysis & Memory Scanning)
Intezer’s Autonomous SOC enriches threat detection and response with capabilities that traditionally required separate tools or expert analysis. It improves detection accuracy and reduces reliance on external analyses by incorporating deep malware analysis and memory forensics.
Originating from "Genetic Malware Analysis," Intezer can analyse files, scripts, and in-memory code to identify malicious behaviour or code reuse. When an alert is triaged, Intezer automatically inspects suspicious binaries or memory snapshots using its proprietary AI and threat intelligence. This built-in capability confirms malware infections on the spot, critical for detecting advanced or fileless attacks requiring memory inspection.
The platform integrates across the broader detection stack: ingesting alerts from endpoints, SIEM, cloud platforms, identity providers, and user-reported phishing emails. This visibility allows the AI SOC to piece together a complete picture of incidents, linking disparate events into a broader attack campaign. Intezer’s system even clusters related threats across multiple environments while maintaining client data separation, enabling MSSPs to quickly flag similar patterns across customers without exposing sensitive data.
By incorporating malware analysis, memory forensics, and broad integrations, Intezer’s Autonomous SOC ensures high detection rates and rich context. The ROI comes from better security outcomes and more efficient operations: fewer incidents go unnoticed, and analysts avoid wasting time on disparate tools. This comprehensive approach reduces risk (by catching complex threats missed by basic SIEM rules) and saves costs on tool consolidation, covering functions that might otherwise require additional products. The platform’s broad coverage amplifies its value, delivering more capability for each dollar invested.
AISOC Autonomous SOC
AISOC.cloud is a UK-based AI-driven Security Operations Centre (SOC) platform (founded in 2025) that augments or replaces key SIEM functions through artificial intelligence. AISOC.cloud sits at the forefront of the emerging AI SOC market, redefining cybersecurity operations through AI-driven detection, correlation, and response automation. Its mission is to democratise cybersecurity by making advanced protection accessible and affordable for organisations of all sizes.
Improved Analyst Productivity and Efficiency
AISOC significantly boosts Tier-1 and Tier-2 analyst productivity by automating the alert triage, enrichment, and correlation processes that traditionally consume the bulk of an analyst’s time. Routine tasks like parsing logs, correlating related events, and gathering context are handled by AI agents, reducing dependency on large analyst teams for first-line monitoring. Key productivity benefits include:
Tier-1 Triage Automation: AISOC’s AI classifiers instantly sift through alerts and flag truly suspicious events, filtering out benign noise. This drastic noise reduction means analysts spend far less time on false positives and mundane alerts, focusing instead on credible threats. In an enterprise SOC, this can eliminate thousands of irrelevant alert investigations per day. For MSSPs, cutting out 80% of alert “noise” across all client environments frees up analysts to manage more customers or perform higher-value threat hunting tasks.
AI-Powered Enrichment & Correlation: AISOC automatically enriches alerts with context (e.g. threat intelligence, asset details) and links related anomalies in real time, tasks that would otherwise require tedious human effort and manual pivoting between different tools. By mapping anomalies to MITRE ATT&CK tactics and techniques, the platform provides instant context on each alert’s significance. This not only speeds up understanding for the analyst but also ensures broader threat coverage without manual research. The built-in MITRE ATT&CK and MITRE ATLAS mapping gives analysts a clear view of the tactics or specific attack vectors involved in an incident, improving investigation quality.
Reduced Workload and Burnout: AISOC’s multi-tenant design allows one AI SOC instance to scale across many clients, effectively “one AI mesh across many customers, multiplying analyst productivity”. An MSSP can therefore support more customer environments per analyst (and lower service delivery costs) while maintaining high-quality monitoring. Enterprise SOCs likewise can repurpose Tier-1 staff to proactive threat hunting or reduce overtime and burnout, knowing that AISOC’s always-on AI is handling the first line of defence.
In summary, AISOC offloads the bulk of repetitive SOC work to intelligent automation. This not only curtails alert fatigue and human error, but translates directly into operational cost savings (fewer analyst hours per incident) and the ability to handle more incidents without linear headcount growth. Higher-tier analysts (Tier-2/3) receive only enriched, high-fidelity alerts, enabling them to focus on complex investigations rather than data gathering. The net ROI is a more efficient and productive SOC that does more with less, which is especially valuable for MSSPs aiming to scale services profitably.
Faster Detection and Response Speed
One of AISOC’s standout ROI drivers is the dramatic improvement in detection and response times. The platform operates in real or near-real time, correlating and assessing events within seconds of their occurrence, as opposed to the minutes or hours that traditional SIEM correlation rules might require. AISOC advertises an under-20-second alert processing time, meaning it can detect and flag threats (including anomalies, insider threats, or zero-day activity) in under 20 seconds from ingestion. This speed has critical implications for containing attacks early and reducing damage:
Real-Time Threat Correlation: AISOC processes and enriches alerts in under 20 seconds using AI-driven correlation and enrichment techniques. Each alert is evaluated in near real time and mapped to frameworks such as MITRE ATT&CK and MITRE ATLAS, enabling analysts to receive fully contextualised, actionable alerts as they arrive.
Sub-20-Second Detection SLA: AISOC often detects and surfaces priority alerts almost instantaneously. The vendor highlights an average detection time in the tens of seconds.
Accelerated Response and Remediation: Beyond detection, AISOC provides incident response recommendations, including immediate actions such as attack containment. It dynamically generates these actions and guidance for detection engineering almost instantly.
Meeting Stringent SLAs: For MSSPs, AISOC’s speed means they can confidently meet aggressive incident response SLAs. Enterprise SOCs benefit by shortening their Mean Time to Respond (MTTR) and improving compliance and risk mitigation — Furthermore, AISOC’s approach to alert triage removes the need to constantly manage automation logic and rule sets used by most SOC’s today.
AISOC transforms SOC operations into a proactive, real-time defence. The ability to identify and neutralise threats within seconds translates into strong ROI through reduced breach impact, analyst efficiency, and SLA fulfilment.
Rapid Deployment and Time-to-Value
AISOC provides fast time-to-value through:
Fast, Flexible Deployment: Cloud-based, with integration and value realisation within weeks.
Pre-Built Integrations: Native support for Microsoft Sentinel, Splunk, Logpoint; roadmap includes Elastic, FortiSIEM, and Wazuh. PSA integrations include HaloPSA and JIRA, with ConnectWise and ServiceNow on the roadmap.
Out-of-the-Box Detection Content: Pre-trained AI and playbooks, ATT&CK/ATLAS mapping, immediate coverage.
OPEX Model: Monthly subscription pricing with predictable cost, aligning with MSSP service delivery models.
This shortens deployment cycles and lowers TCO. MSSPs onboard clients faster; enterprises accelerate value realisation.
Threat Coverage and SIEM Integration
AISOC provides:
Broad Threat Coverage: AI/ML models enriched with MITRE ATT&CK and ATLAS mappings.
Global Threat Intelligence: Contextualises alerts with known IoCs and adversary behaviours.
SIEM-Centric Architecture: AISOC ingests all alerts from SIEMs (Microsoft Sentinel, Splunk, etc.) and learns over time based on telemetry and security analyst’s feedback, preserving compliance and centralised visibility.
Streamlined Workflows: PSA and ITSM integrations (HaloPSA, JIRA; roadmap: ConnectWise, ServiceNow) ensure alerts and insights are embedded into the SOC workflow.
This architecture provides MSSPs and enterprises with AI-enhanced security operations without replacing their SIEMs, increasing effectiveness and preserving investments.
Qevlar AI
Analyst Productivity Gains
Qevlar AI is designed to function as an autonomous Tier-1 SOC analyst, dramatically reducing the manual work required for alert triage and investigation. It automatically enriches alerts with context from threat intelligence and internal data sources, then correlates findings to determine if an alert is truly malicious. This offloads the tedious first-line analysis from human analysts. In practice, organisations using Qevlar report up to a 90% reduction in Level-1 and Level-2 analyst workload on routine tasks. For example, alerts that once took a human 30–40 minutes to investigate can be handled by Qevlar in about 3 minutes. By autonomously closing 100% of benign/false-positive alerts, Qevlar ensures analysts spend time only on meaningful threats. This efficiency translates into significant ROI: Qevlar advertises a minimum 3× return on investment for SOC teams, and some users have seen on the order of 10× productivity improvements in terms of alerts handled per analyst. For MSSPs, these gains mean the ability to support more customers with the same staff. In fact, MSSPs like Atos and Nomios publicly state that Qevlar’s autonomous investigations free up their human experts to focus on proactive threat hunting and custom detections, scaling their capacity without linear headcount growth.
Detection and Response Speed
By automating the triage process, Qevlar AI significantly accelerates threat detection and response times in the SOC. It operates in real time: as soon as an alert is generated by any integrated tool (SIEM, EDR, etc.), Qevlar’s agent springs into action to investigate 24/7, within seconds or minutes rather than hours. This always-on, immediate investigation model yields a Mean Time to Investigate (MTTI) of around 3 minutes on average: although Qevlar typically stops short of executing responses itself (it provides recommended next steps rather than performing them autonomously), it enables the SOC to move from detection to remediation much sooner. Analysts receive a comprehensive incident report moments after an alert, allowing them to confirm the threat and initiate containment or remediation procedures without delay. Organisations using Qevlar have noted that critical incidents can be identified and escalated in minutes, even during off-hours. Notably, Qevlar maintains high accuracy (99.8%) in its alert verdicts, which means fewer false alarms slow down response. This high fidelity combined with speed improves overall MTTR. In essence, Qevlar shifts the SOC from reactive, queue-based analysis to a proactive stance where potential threats are investigated and validated nearly as fast as they’re generated, dramatically cutting both MTTD and MTTR in day-to-day operations.
Time-to-Value and Deployment
Qevlar is delivered as a pre-trained, cloud-based AI platform, which means deployment is fast and upfront effort is minimal. SOC teams do not need to write new detection rules or playbooks to start using it. Unlike traditional SOAR tools that demand extensive playbook scripting and tuning, Qevlar comes ready out-of-the-box and it connects into your existing security stack and starts autonomous investigations from day one. The integration process is intentionally lightweight: no complex setup scripts are required, and integration with various SIEMs, EDRs, ticketing systems, and data sources is supported via API, allowing Qevlar to “plug in” to whatever tools you already use. Qevlar states “stack freedom” and no vendor lock-in, meaning you can feed it alerts from any environment or technology stack without friction. This approach yields near-immediate value as the Qevlar enterprise solution brief notes, it is “quick and easy to integrate, and delivers high-impact results immediately.” Early adopters have reported that after a short onboarding, Qevlar began handling alerts and producing useful investigation reports right away, demonstrating value in the first 24 hours. The platform’s pricing model (flat yearly fee based on alert volume, with unlimited analyst seats) also simplifies procurement and scaling, avoiding surprise costs as usage grows. Overall, Qevlar’s ease and speed of deployment translate to a short payback period on investment. The SOC starts saving analyst hours and catching threats more efficiently almost as soon as Qevlar is turned on, rather than waiting for a long tuning period.
Threat Coverage and Integration Model
Threat Coverage: Qevlar’s autonomous SOC analyst is built to handle alerts across all major attack vectors. It’s agnostic to alert source or type, whether it’s a phishing attempt, endpoint malware alert, cloud security event, or identity abuse warning, Qevlar will investigate it with the same rigour. The platform continuously learns and adapts to new threat patterns, rather than relying solely on static detection rules. This means it can adjust to novel or evolving attack techniques and still provide a conclusion about an alert’s maliciousness. By leveraging a combination of an internal knowledge graph and large language models (as noted by its developers and investors), Qevlar correlates data points across an environment to see the full context of an incident. Notably, Qevlar does not generate its own alerts from raw log data in the way a SIEM might; instead, it works on alerts from existing tools. Thus, its threat coverage is as broad as the detection coverage of your stack. The added value is that no alert goes unanalysed. Qevlar will triage 100% of alerts fed into it, even those a human team might ignore due to volume, thereby covering potential threats that might slip through in a busy SOC.
Integration Model: Qevlar is explicitly positioned as a SIEM/XDR augmentation layer rather than a rip-and-replace SIEM solution. It integrates with whatever detection and logging tools the organisation already uses (Splunk, Microsoft Sentinel, CrowdStrike, etc.) via APIs and connectors, pulling in their alerts and additional data for enrichment. It also reaches out to external sources like threat intelligence feeds, cloud provider logs, asset inventories, and more to gather context during investigations. This broad integration ecosystem allows Qevlar to act as an overlay “brain” on top of the existing SOC infrastructure, maximising the ROI of tools you’ve already invested in. Rather than storing full log data itself, Qevlar analyses alerts on the fly and produces a report; long-term log retention and compliance archiving remain tasks for traditional SIEM or data lake solutions. In compliance-focused environments, Qevlar has been used in a way that supports audit and regulatory needs: it provides transparent, traceable investigation reports for each alert, and even allows analysts to review every step the AI took (nothing is a black box). These detailed reports can serve as evidence of due diligence and can be archived for compliance or used in post-incident analysis and training. The platform’s design assumes a “human in the loop” for final validation and remediation decisions, which aligns with regulatory best practices that require human oversight for critical security decisions. Finally, Qevlar is proven to function in multi-tenant MSSP environments: it can segregate data and tailor its analysis per client organisation, as evidenced by its successful deployments with large MSSPs (Atos, Almond, etc.) who manage regulated industry clients. In summary, Qevlar cleanly augments a SOC’s capabilities, covering the investigative aspect without disrupting existing tool investments or compliance mandates. It replaces up to “80% of traditional SIEM functions” like ingestion, correlation and first-level analysis, while relying on the SIEM for the remaining needs such as long-term log storage and niche legacy integrations.
Dropzone AI – ROI Analysis
Analyst Productivity Gains
Dropzone AI markets itself as “the world’s first AI SOC analyst” that replicates the techniques of elite human analysts to autonomously handle Tier-1 alert triage and investigation. By doing so, it delivers substantial improvements in SOC productivity. A key outcome of deploying Dropzone is that it ensures every alert is investigated thoroughly without additional human effort. In a conventional SOC, resource constraints mean analysts can only manually address a fraction of incoming alerts (often as low as ~30%). With Dropzone, organisations report approaching 100% alert coverage, since the AI investigates alerts continuously, 24/7. This guarantees that no alert is overlooked due to volume, effectively multiplying the SOC’s capacity. At the same time, Dropzone drastically reduces the work per alert. The platform performs data enrichment, log querying, and cross-correlation automatically, presenting the analyst with a ready-made investigation report. This has been shown to cut the average manual analysis time per alert from about 25 minutes to roughly 2 minutes of human review. Over 90% of the toil in triage can be eliminated, freeing analysts from spending ~80% of their day on initial alert handling down to about 5%. The productivity impact is that a small team can do the work of a much larger one. Many users also highlight quality of life improvements: with mundane tasks offloaded, analyst morale goes up and burnout goes down. For MSSPs, the multi-client equivalent is transformative as Dropzone is built to be multi-tenant, enabling one AI analyst instance to serve many customers in parallel. This allows MSSPs to scale their operations without one-to-one staffing increases, maintaining quality of service across clients while onboarding new ones rapidly. 
Detection and Response Speed
Speed is a core value proposition of Dropzone AI. By automating triage, Dropzone can react to security alerts immediately and investigate them within minutes. This real-time investigation capability drives down both detection and response intervals. Notably, Dropzone enables organisations to achieve single-digit-minute response times in many cases. One published benchmark indicates the system can help “contain threats in under 10 minutes,” radically shrinking Mean Time to Respond (MTTR) for high-priority incidents. In practice, users have seen improvements like a 5× faster response time after deploying Dropzone. The platform accomplishes this by not only analysing alerts quickly but also by taking automated actions for containment when configured. Dropzone’s architecture supports “auto-containment actions”, for example, isolating a host via EDR or disabling an account, triggered by its verdict on an alert, so that fast-moving threats can be stopped. This can dramatically cut Mean Time to Contain, an element of MTTR. Even when full automation of response isn’t used, Dropzone accelerates the process by handing analysts a complete investigation on a platter. An alert that might have sat in a queue for 30–60 minutes before an analyst picked it up will, with Dropzone, already have a detailed report within a couple of minutes of its arrival. This means an analyst’s first touch is often to initiate remediation, not to gather information. In terms of detection (MTTD), Dropzone ensures that no true threat goes unnoticed for long: it investigates 100% of alerts and flags the malicious ones with evidence, effectively reducing the chance that a stealthy threat languishes undetected in the alert queue. Industry experts note that this level of cognitive automation thoroughly investigates alerts in a way traditional playbook-driven tools cannot, bringing a “step function” improvement to SOC speed and thoroughness. For MSSPs, faster detection and response mean they can meet tight SLA commitments and differentiate on superior incident handling times. Overall, Dropzone shifts the SOC towards real-time, around-the-clock threat mitigation. 
Time-to-Value (Deployment and Onboarding)
Dropzone AI prides itself on rapid deployment and immediate results, which is critical for ROI realisation. The solution is available as a cloud-based platform that can be deployed in minutes. Dropzone’s own materials highlight that it “deploys in minutes” and delivers value on Day 1. Setting it up typically involves connecting API keys or log source credentials for the tools you already use. Because Dropzone comes with built-in knowledge of common alert types (phishing, cloud threats, endpoint malware, etc.), it provides effective out-of-the-box analyses without requiring the user to create detection content or playbooks. The platform also offers a self-guided demo and pre-sales trial environment so teams can see it in action on sample alerts, further shortening the evaluation and onboarding cycle. In production, integrating Dropzone into workflows is straightforward: it connects to popular SIEMs and ticketing systems so that whenever an alert is created, Dropzone automatically generates an investigation report and can even update tickets with its findings. Its extensive library of integrations spanning Microsoft Sentinel, Splunk/QRadar, CrowdStrike, Microsoft Defender, Okta, AWS, email gateways and more means most organisations can hook up their stack to Dropzone with minimal engineering effort. Additionally, Dropzone’s vendor provides “top-tier customer support for every account”, which can be invaluable in quickly resolving any integration issues or tuning needs during deployment. The multi-tenant design also accelerates time-to-value for MSSPs: an MSSP can deploy one Dropzone instance to serve many clients, and onboard each new client environment by simply adding their data connectors, rather than deploying a whole separate stack per client. In summary, Dropzone’s fast deployment and immediate efficacy mean that organisations begin accruing the benefits (alert coverage, time saved, risk reduced) almost as soon as the product is enabled. The payback period is short, and the simplicity of setup avoids heavy upfront professional services costs, thereby reinforcing a strong time-to-value proposition.
Threat Coverage and Integration Model
Threat Coverage: Dropzone AI is built to handle a wide spectrum of SOC use cases out-of-the-box. The platform comes pre-trained to investigate alerts across categories such as phishing emails, endpoint malware or EDR detections, network intrusion alerts, cloud security events, identity and access anomalies, and even insider threat indicators. This broad coverage means Dropzone can function in diverse environments (on-prem, cloud, hybrid) and address many attack vectors. By “replicating the techniques of elite analysts,” Dropzone doesn’t just check static rules, it dynamically pulls in context (user details, asset criticality, threat intel, historical events) to understand each alert in depth. This dynamic enrichment allows it to handle novel threats better than a rigid rule-based system; if an alert is out of the ordinary, the AI will research it (e.g. query logs, parse scripts, consult intelligence sources) much like a human would, potentially catching malicious activity that wasn’t explicitly known beforehand. However, it’s important to note that Dropzone’s scope is alerts, it extends and completes the investigation of alerts generated by other systems. In that sense, its coverage depends on the detections coming from SIEM, EDR, cloud monitors, etc. It greatly enhances what you can do with those detections (thoroughly investigating every alert every time, as one expert observed), but it is not itself a full detection engine for raw telemetry. Nonetheless, by ensuring even low-confidence or minor alerts get analysed, Dropzone can surface real incidents that might have been missed by overwhelmed teams. Its users have testified that “even resource-constrained organisations can now focus on the security alerts that matter,” because the AI handles the heavy lifting on all alerts and highlights the truly important ones. Importantly, Dropzone’s continuous learning capability means the more you use it, the more tailored it becomes to your environment. Dropzone’s “context” feature automatically gathers and unifies relevant data from multiple sources such as SIEM, EDR, cloud, identity, and threat intelligence, to enrich each alert investigation with user, asset, and historical insights. This gives analysts a complete, high-fidelity view of the alert’s significance and surrounding activity, enabling faster, more accurate decision-making without manual data hunting as It “remembers details” about each organisation’s systems and adjusts its investigative reasoning accordingly. This improves accuracy and reduces false positives over time, enhancing effective threat coverage as the system matures in a given environment.
Integration Model: Dropzone is positioned as an augmentation layer to existing SOC infrastructure rather than a standalone SIEM replacement. It integrates with a wide ecosystem of tools: SIEM platforms (e.g. Splunk, Microsoft Sentinel, IBM QRadar, Google Chronicle) feed it alerts, EDR and XDR solutions (CrowdStrike, Cortex XDR, SentinelOne, etc.) provide alerts and additional endpoint context, cloud security platforms (AWS/Azure/GCP services, Wiz, etc.) send cloud threat findings, and so on. Dropzone essentially sits on top of these, ingesting their output (alerts/events) and then using APIs to pull more information from them during investigations. It also ties into case management or ticketing systems, for instance, ServiceNow or Jira so that when an alert ticket is created, Dropzone’s results can be inserted directly into the ticket workflow. This tight integration means analysts don’t have to swivel-chair across tools; the context from many sources is unified in Dropzone’s report. Rather than replacing your SIEM, Dropzone aims to “make the most of your SIEM investment” by ensuring the alerts it generates are fully analysed and acted upon. It can  reduce the noise and overwhelm typically associated with SIEM alerts, effectively functioning as an AI co-pilot (or in their terms, an AI “teammate”) to your human analysts. In terms of compliance and trust, Dropzone is designed with a human-in-the-loop philosophy. It always shows its work to the user, meaning analysts can review the evidence and reasoning behind the AI’s conclusions. This transparency is crucial in environments like finance or healthcare where auditors or clients might require proof of how an incident was handled. The Dropzone platform allows for human validation at the end of the investigation and does not enforce automated action without approval (unless configured to do so). This balances efficiency with control. Additionally, Dropzone’s multi-tenant architecture is a major integration advantage for MSSPs: a provider can run one logical instance and segregate data per client, with Dropzone learning each client’s context separately. It effectively creates an “AI SOC army” that is centrally managed but securely serves many end-customer environments. Finally, pricing-wise, Dropzone is offered as a subscription (starting in the tens of thousands of USD per year range, depending on scale), which for many organisations is less than the cost of a full-time Tier-1 analyst. Considering it can take on work equivalent to multiple analysts, the cost model further underscores its ROI in augmentation mode. This integration-first model helps ensure that adopting Dropzone yields positive results without upheaval, making it a practical and high-ROI addition to both enterprise and MSSP SOC environments
ROI Summary: Key Findings Across Platforms
In conclusion, each platform offers ROI in different ways. Established players like Splunk and Microsoft Sentinel bring broad capabilities with Sentinel demonstrating a high ROI through cloud efficiencies and automation, while Splunk offers deep flexibility but at a higher cost that must be justified by maximising its use. Cloud-native solutions like Chronicle, Panther, and Sekoia emphasise lower cost per data and rapid deployment, turning big-data scale into an ROI advantage (ingesting everything for better security at a fraction of legacy cost). XSIAM and Exabeam showcase the power of integration and analytics, their users see sizeable efficiency gains and risk reduction by unifying detection across vectors. SentinelOne proves that automated endpoint protection can virtually eliminate certain manual efforts and breach costs, yielding one of the highest ROIs if evaluated purely on security outcomes. Finally, the emerging AI-SOC platforms (Prophet, Anvilogic, Intezer, AISOC.) are pushing ROI to new heights by overlaying intelligence on existing systems by effectively multiplying the impact of current tools and personnel and also handling the heavy lifting of analysis. These AI-driven solutions can drastically cut operational costs and improve response metrics, making them attractive to both enterprises and MSSPs aiming to "do more with less" in the SOC.
When evaluating across MSSP vs End-User scenarios, the common theme is that ROI is maximised by: automation, scalability, and consolidation. End-user organisations benefit when a platform reduces their need to add headcount or overspend on infrastructure, and when it quickly bolsters their security posture (preventing incidents that would be far costlier than any tool). MSSPs, on the other hand, value multi-tenancy, flexible licencing, and the ability to streamline operations across clients. Platforms that allow one SOC team to safely monitor many customers (through strong automation and separation) provide a clear ROI path in the MSSP business model and where the AISOC platforms are already making an impact.
The platforms above have strengths in certain ROI criteria and potential drawbacks in others. The table and analysis aim to make these trade-offs clear for readers of this study. Ultimately, an organisation or service provider should align their choice with their primary ROI goals, whether it's cutting licencing costs by 80%, halving their MTTR, or doubling the productivity of their analysts, there is likely a platform (or a combination of a SIEM + AI-SOC layer) that can achieve that, as evidenced by the real-world examples.
Sources: The analysis above is supported by industry studies and benchmarks including an IDC study on Google Chronicle, Forrester Total Economic Impact reports for Microsoft Sentinel, Exabeam, and SentinelOne, Peer insights and MSSP case studies for platforms like Splunk and Sekoia, and vendor data from Prophet Security and Anvilogic, AISOC, Dropzone among others. Each data point illustrates how these platforms perform in real deployments, painting a comprehensive comparative ROI picture.
AI SOC Mesh - An Evolving Framework
This visual represents the AI SOC mesh, an integrated framework designed to revolutionise traditional Security Operations Center (SOC) functions by embedding artificial intelligence across key operational layers. It moves beyond isolated point solutions, fostering a collaborative ecosystem where AI augments human capabilities, automates routine tasks, and accelerates threat detection and response. This approach signifies a fundamental shift from reactive, human-centric security to proactive, AI-assisted defence, directly addressing the growing volume and sophistication of cyber threats, as well as the chronic shortage of skilled cybersecurity professionals. This evolution could solve the shortage of skills in a different way, by teaching junior analysts through explained incident narratives so that the analyst learns faster than following manual playbooks and evolves into a multi skilled engineer who can pivot from detection oversight to analytics optimisation as well as deploying security remediations across protective tooling and understanding offensive techniques to hunt for edge cases, where only human intuition and institutional knowledge can bring nuance, and where LLM's/ML reach their contextual limits.
The AI SOC mesh brings three core elements together, illustrating where many AI SOC vendors are focusing their innovations to create a more resilient and efficient security posture. These interconnected layers work in concert to enhance the overall security lifecycle:
1. AI-Enhanced Analyst Productivity (This study's Main focus)
This foundational layer dramatically increases SOC analyst efficiency by automating repetitive tasks like alert triage and data correlation, reducing alert fatigue and enabling focus on strategic work. This leads to reduced mean time to investigate (MTTI), lower operational costs, and decreased analyst burnout (e.g., Prophet, AISOC, Anvilogic). Challenges include ensuring AI trustworthiness and integration with existing tools, while opportunities lie in developing sophisticated, adaptive AI assistants.
2. AI Detection Layer
This layer focuses on advanced, AI-driven threat detection using machine learning models to identify anomalous behaviors and novel threats, moving beyond static, signature-based rules. It processes vast security telemetry in real-time, significantly improving detection rates for unknown threats and reducing false positives (e.g., Panther). More mature than the AI Response layer, its evolution involves continuous adaptation through self-learning models. Key challenges include high-quality training data and explaining AI's rationale, with opportunities in federated learning and self-healing detection systems.
3. AI Response Layer
The AI Response layer orchestrates incident remediation by guiding or executing automated responses, such as system isolation, IP blocking, or data enrichment. This reduces mean time to respond (MTTR) through faster containment and efficient coordination (e.g., Binalyze). This layer is less mature due to the complexities of incident coordination, legal/compliance considerations, and the technical depth required for DFIR tasks. Challenges involve ensuring safe, auditable AI-driven responses and maintaining human oversight, while opportunities include advanced AI-driven playbook generation and predictive incident response.
Interoperability and Evolution: These three layers are not siloed; they are deeply interconnected. Enhanced analyst productivity relies on robust AI detection to surface relevant alerts and on AI response capabilities to automate initial actions. Similarly, effective AI detection informs and refines the AI response mechanisms. The evolution from traditional, siloed SOC operations to this AI-enhanced mesh involves breaking down organisational and technological barriers, which will bring problems of it own. This is a considerable shift towards  integrating diverse security tools, and fostering a culture of automation and continuous learning.
ROI Implications: Implementing an AI SOC mesh offers significant ROI advantages over traditional point solutions. By synergistically integrating these AI capabilities, organizations can achieve.
The AI SOC mesh represents the future of security operations, moving towards a more intelligent, automated, and human-augmented defence strategy. Future trends include greater predictive capabilities, self-optimising security controls, and increasingly sophisticated human-AI teaming to stay ahead of adversaries.
Contact Cyber3D
Cyber3D specialises in SOC advisory and consulting, helping businesses secure their digital assets and navigate complex threats. Our expertise spans threat detection, incident response, compliance, and strategic SOC consulting.
Partner with Cyber3D to strengthen your SecOPs. Contact us today for a free consultation!
Cyber3D
Email: steve.eyre@cyber3d.uk
Website: www.cyber3d.uk