Attackers using AI can go from vulnerability discovery to breach in 22 seconds.
Your SOC team, on average, checks that alert 8 hours later. By then, it's already over.
That's the gap $125 million just went to close.
So what even is an AI SOC?
A Security Operations Center (SOC) monitors your company's security around the clock. The problem? That's becoming genuinely impossible for humans to do at scale.
Of the thousands of alerts that pour in every day, 95% are false positives. Analysts spend most of their time sifting through noise to find the one real threat. At RSAC 2026, this dynamic was officially declared a war "moving faster than humans can track".
An AI SOC hands that repetitive work over to AI agents — and it's different from SOAR's playbook approach. These agents understand context, gather evidence, reach a verdict, and execute a response. Like a real analyst would.
Exaforce implements this across four AI agents called Exabots. Three years in, the startup just closed a $125M Series B at a $725M valuation — with Khosla Ventures, Mayfield, and HarbourVest in the round.
- Exabot Detect
Monitors AWS, Okta, GitHub, Slack, OpenAI, Google Workspace, and more in real time. Combines behavioral baselines with contextual intelligence to flag real breaches and reduce false positives. - Exabot Triage
Autonomously investigates incoming alerts and delivers a verdict: false positive, benign, or needs attention. Filters out 70% of alerts before an analyst ever sees them. - Exabot Investigate
Natural language interface for querying security data across environments. No SQL required — just ask "were there any suspicious logins in the past 30 days?". - Exabot Respond
Executes complex response workflows — MFA resets, session terminations, device containment — with built-in error handling.
Here's the thing: they're not just running a single LLM. Exaforce uses a Multi-Model AI engine combining Semantic, Behavioral, and Knowledge models. The goal is to avoid the hallucination, inconsistent reasoning, and high-cost data processing that come with relying on an LLM alone.
How is this different from existing security tools?
SIEM and SOAR are widely deployed — so what makes an AI SOC worth rethinking your stack?
| Area | Legacy SIEM/SOAR | AI SOC (Exaforce) |
|---|---|---|
| Alert handling | Playbook-based triage (humans write & maintain) | AI understands context and decides autonomously |
| Investigation | Analysts run SQL/queries manually | Natural language questions, AI gathers evidence |
| False positives | Analyst reviews each one | 70–95% auto-filtered |
| Response time | Detect-to-response: hours on average | Investigation in 60 seconds, auto-execution |
| Data costs | Ingest volume-based pricing | Intelligent storage tiering cuts costs |
The technical differentiator is when context gets built. Most competitors reconstruct context after an alert fires. Exaforce builds a real-time security knowledge graph at ingestion — so threat detection is higher-fidelity and token costs per investigation drop significantly.
Real customer results
Accton reduced mean time to investigate (MTTI) from 3 hours to 10 minutes. Commonwealth Fusion Systems cut cloud log storage costs by 90%. Automation Anywhere reduced investigation workload by 50%. On Gartner Peer Insights, Exaforce holds a 5.0/5 rating.
The competitive field is crowded: 7AI, Dropzone AI, Prophet Security, CrowdStrike Charlotte AI, Radiant Security. CrowdStrike claims 98% triage accuracy; Radiant Security claims 90% false positive reduction. In GigaOm's inaugural SecOps Automation Radar, Exaforce landed as a Leader and Outperformer.
"When the cost of defense drops by an order of magnitude, the entire calculus of security changes."
— Vinod Khosla, Khosla Ventures
핵심만 정리: 시작하는 법
- Baseline your current SOC
Measure daily alert volume, false positive rate, and per-analyst throughput. The ROI of an AI SOC becomes clearest when these numbers are large. - Check your integration surface
Exaforce supports AWS, Okta, GitHub, Slack, Google Workspace, Azure, and CrowdStrike EDR, among others. Map how much of your current stack is already covered. - SaaS vs. MDR
Have an internal team? Go SaaS. No dedicated security staff? Start with Managed Detection & Response. Exaforce offers both. - Pilot → first automated response
Average time from onboarding to first automated response is under 30 days. Start in "Human-on-the-Loop" mode during the pilot to validate AI judgment before going fully autonomous. - Track three metrics
MTTI (mean time to investigate), false positive rate, analyst hours saved per month. These three tell you whether the AI SOC is actually working within six months.
Heads up
An AI SOC is not a headcount reduction tool. The point is to free analysts from repetitive triage so they can focus on threat hunting and strategic decisions. "AI handles it all, so we can cut the team" is the wrong takeaway — accountability for AI misjudgments still rests with humans.
