The gap is not what you think it is
Here is the number that should bother you. Your enterprise SIEM has enough telemetry to detect more than 90% of MITRE ATT&CK techniques. It is actually detecting 21% of them. That figure comes from CardinalOps’ 2025 annual report, and it is not a vendor talking point — it is a structural indictment of how alert-first security operations work.
Worse: 13% of the rules you do have are broken. They will never fire. So the coverage number is already optimistic.
Meanwhile, 40% of security alerts are never investigated at all, according to SACR’s 2025 AI SOC Market Landscape report. Nearly half of the ones that are investigated turn out to be false positives, per the SANS 2025 Detection and Response Survey. Your analysts are drowning in noise, missing signal, and — according to Splunk’s 2026 security predictions — more than 50% of them are considering quitting in the next twelve months.
This is not a headcount problem. It is a model problem. And the model is finally changing.
Alerts are the wrong starting point
The classic SOC workflow runs like this: telemetry flows in, rules fire alerts, analysts triage alerts, and investigations follow from there. In theory, the rules do the heavy lifting. In practice, as Cybereason has documented, a SIEM cannot even handle the volume of endpoint logs required to build real visibility — let alone correlate them with network, cloud, and identity telemetry. The result is an expensive compliance tool masquerading as a detection engine.
Traditional teams analyze individual alerts in isolation. But attackers do not operate in isolation. Privilege escalation, lateral movement, and data staging are coordinated sequences. An analyst working a single alert queue will see each step as a separate, low-priority ticket. By the time the pattern is obvious, the dwell time is already measured in days.
So the question worth asking is not “how do we triage faster?” It is “what if we started from the telemetry instead of the alert?”
What hunt-first actually means in 2026
Threat hunting has historically been a luxury. When 61% of teams already cite staffing shortages as their top barrier — as the SANS 2025 Threat Hunting Survey found — dedicating analysts to proactive investigation feels like something only well-resourced teams can afford. That calculus is shifting fast.
Agentic systems change the throughput equation. A hunt is no longer limited by how many queries an analyst can manually write, or how much telemetry fits in a single shift. Agentic workflows can automate pivots, enrich context, and test hypotheses across endpoints, identity, cloud, and SaaS data simultaneously. Hunting becomes a repeatable control-validation activity rather than an incident-response luxury.
The research backs this up. A 2025 survey of agentic defensive systems — published on arXiv — identified detection as the most common application, with 19 papers covering systems like TTPDetect and LLMCloudHunter. But threat hunting received meaningful attention too, with 10 papers covering systems like CyberRAG, CTI-REALM, and ProvSeek. These systems combine long-term knowledge stores with proactive investigation. The architecture insight matters: the best agentic systems ingest raw telemetry first, then surface alerts as outputs — not as inputs.
In practice, this means an agent can watch for the quiet precursors that never trigger a rule: a user enumerating shares at 2 a.m., a service account authenticating to an unusual subnet, a cloud storage bucket quietly staging files. None of those events is individually alarming. Together, they are a story.
The small-team edge case nobody mentions
There is a real caveat here, and I would rather name it than let you find it the hard way. In environments with limited telemetry maturity, an agentic system can accelerate bad assumptions as fast as it accelerates good ones. If your log coverage is thin, the agent will hunt confidently across a partial picture. Garbage in, confident garbage out.
So before you deploy an agentic hunting layer, the honest first step is a telemetry audit. Start with platform-native capabilities — cloud providers, container orchestration, and CI/CD tooling all include built-in telemetry that requires minimal configuration. Enable those first. Prioritize critical security boundaries and high-risk components rather than attempting comprehensive coverage immediately.
Once telemetry quality is reasonable, the operational model for small teams typically looks like this: the agent runs continuous hunts against raw telemetry, surfaces correlated cases rather than individual alerts, and flags high-confidence findings for human review before any containment action executes. Human approval before high-impact response is not optional — it is the architectural feature that keeps the system trustworthy.
The arXiv survey describes this pattern explicitly: agentic defensive systems “frequently incorporate human approval before executing high-impact responses.” That is not a limitation. That is the design.
Measuring whether it is working
The metric that matters most here is ATT&CK detection coverage delta — the change in percentage of MITRE ATT&CK techniques your rules cover before and after a hunting campaign. If hunts are surfacing real gaps and you are converting findings into new detection rules, that number should move. If it is not moving, the hunts are not improving your posture. They are just generating reports.
Secondary metrics worth tracking: mean time from hunt hypothesis to confirmed finding, false positive rate on agent-surfaced cases versus rule-triggered alerts, and the percentage of ATT&CK techniques with at least one validated detection. Together, these tell you whether the system is learning or just running.
The SANS 2025 Threat Hunting Survey found that most organizations struggle to demonstrate hunting’s business value to leadership. When staffing is already thin, that measurement gap is dangerous — prove value or risk having the program cut. Agentic systems help here too, because they generate structured logs of every hypothesis tested, every pivot taken, and every finding surfaced. That audit trail is the evidence your leadership needs.
One thing to do this week
Pull your current SIEM rule set and run it against the MITRE ATT&CK Navigator. Map which techniques you have active, validated rules for — not just rules that exist, but rules that have fired at least once in the last 90 days. That is your real detection coverage number. In our experience, most small teams find it is well below what the dashboard claims.
That gap is where agentic hunting earns its place. Start there.
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