Creating a shortlist for an AI SOC assessment can be difficult. SIEM, SOAR, and pure-play AI SOC vendors all say the same thing. But behind the same label are entirely different products, from chat assistants bolted onto traditional SIEMs to agent platforms that run discovery, triage, investigation, and response on their own data foundations.
Whether a platform significantly changes team outcomes is more important than the name of the platform. You can measure investigation time, amount of false positives, analyst return time, total cost of running a SOC, and ultimately whether your architecture will hold up in two to three years as the volume, velocity, and complexity of attacks continue to increase.
What is an AI SOC platform?
An AI SOC platform is a security operations platform where AI agents perform core SOC tasks (detection, triage, investigation, and response) by reasoning on correlated security data under human supervision. This differs from bolt-on AI, which summarizes alerts within your existing SIEM while the underlying work remains manual.
When vendors refer to agents, they mean agents that perform core tasks. While the distinction may seem subtle on the datasheet, the real proof is during the POC.
What makes an AI SOC agent predictable?
Predictability differentiates reliable SOC automation from babysitting automation. Predictability is more of a data property than a model property. Agents that only summarize alerts can operate solely from alert payloads. A trusted agent that closes an alert or performs a response action requires more context, such as the entity involved (identity, resource, device/asset), how its configuration is drifting, the entity’s healthy state, and many other factors.
A platform built for that level of trust maintains a real-time knowledge graph, a continuously updated map of the identities, resources, configurations, and behavioral baselines in your environment and the relationships between them, assembled before an alert occurs. Based on that context, combined with the layered model architecture described in the checklist below, the agent returns a verdict that is supported by consistent evidence. Bolt-on AI works in the opposite direction, querying the raw logs after an alert has been raised. As a result, its conclusions often do not hold up under scrutiny. Width is equally important. The most powerful platforms add detection coverage of never-before-measured sources, perform continuous threat hunting, and initiate response to incidents as they unfold.
6 AI SOC Features to Test Before Buying
You can see each of the features below in action during a proof of concept, in your own environment, or in a vendor demo.
- Real-time correlated data infrastructure. AI decisions are determined by the context behind them. Ensure that identity, configuration, resource, and baseline data are continuously correlated (knowledge graph approach) or assembled from raw logs at query time. Speed alone doesn’t mean much. A fast query engine also returns responses in seconds. Instead, randomly select an identity and understand its privileges (administrator or not), configuration drift, and behavioral baseline (typical location, IP, ASN, user agent, etc.). These cannot be spoofed at query time.
- Full life cycle agent. Have your vendor run a single incident end-to-end, from source detection to triage, investigation, and response actions, and watch as context is carried forward or re-gathered across each step. Many platforms automate Tier-1 triage and stop there, thus speeding up the alert queue without speeding up the SOC.
- Auditable verdicts supported by evidence. Ask to see the evidence behind the verdict (all the log lines, correlations, and what generated the inferences) and make sure the analyst can reproduce the results from the same data. A verdict that cannot be audited is an opinion.
- Detection range beyond SIEM. Real-world incidents span cloud, SaaS, identity, and code, but much of the telemetry never makes it to the SIEM because it’s too expensive to ingest. List the sources your stack leaves implicit, such as high-volume cloud audit logs, GitHub, and Google Workspace, and ask your vendor to demonstrate launching detections against and investigating them.
- Gradual autonomy with human supervision. Full autonomy from day one is a red flag, as is a platform that never makes more than read-only access. Investigate how trust is configured, which actions are initiated as recommendations, which evidence records unlock automatic execution, and where users sign off. Notice that you can adjust these thresholds for each action type.
- Measurable outcomes. Before starting a POC, define numbers such as false positive rate and average time to investigate and respond. Measure your results against your current baseline and ask your reference customers what happened in the first quarter. If you ultimately want a vendor to run it for you, make sure the managed service uses the same products that your team operates on.
Spotlight: Exaforce’s Agentic SOC Platform
One platform designed around these capabilities is Exaforce. Agent AI SOC platform. Its four Exabots cover the entire SOC lifecycle. Exabot Detect acts as an AI detection engineer, Exabot Triage drives all alerts to a decision at Tier 3 depth, Exabot Investigate reduces barriers to threat hunting, and Exabot Respond coordinates actions across the kill chain with human approval of the irreversible.
All four Exabots infer a unified real-time data platform that ingests and powers logs and configuration across cloud, SaaS, identity, endpoints, and code. Analysts query everything in plain language through Exabot. It can replace a SIEM on the same platform, but without the parsers, pipeline maintenance, and hiring of SIEM experts that typically come with it. Guardant Health has made Exaforce its primary SIEM and MDR. “I don’t write queries anymore; I just ask Exabot,” says Mike Shannon, director of security engineering at Guardant Health.
The measured results correspond to the above functions. Invisible cuts reduce investigation time by 95%, turning investigations from hours and days to minutes. Forcepoint replaced MSSP with Exaforce MDR, which required manual response, and P0 incidents now take an average of 14 minutes to respond.
You can choose to run the platform with your in-house team or have it operated through Exaforce’s MDR product. The architecture and Exabot are the same in both cases. Only the person operating it changes.
How close are we to an autonomous SOC?
No other platform, including Exaforce, can solve modern SOC problems. The battle is AI versus AI, and what is won is not the frontier model but the data the agent infers. Based on real-time data associated with identities, assets/devices, affected resources, and baseline behavior, agents generate predictive, repeatable, and auditable decisions, giving confidence to humans leveraging AI in the SOC.
To get started with your evaluation, Exaforce’s unique introductory guide, What is an AI SOC?, provides a starting point. Next, put the above six capabilities in front of every vendor on your shortlist and request a demo to see how Exaforce answers those capabilities.
