AI helps this, but it doesn’t solve it. As Prophet Security, leading AI SOC company, states, “AI can identify patterns and even provide a natural language interface to make threat hunting easier for anyone, but…we’re not at a point where AI can autonomously execute threat hunts.”
That changes coverage. More hypotheses can be tested. Weak signals that would normally be ignored become worth checking. Control gaps can be identified internally before they become incidents, and hunting becomes less dependent on a small number of seasoned senior analysts who are using their expertise to connect log sources by hand.
Threat hunting is the proactive process of searching for signs of malicious activity that may have bypassed automated security tools and traditional alerting systems. It assumes that attackers may already be operating inside the environment without detection, using legitimate tools and stolen credentials to blend into normal activity.
Analysts can spend days or even weeks chasing a headline campaign while overlooking less-obvious compromises in their own backyard: stale service accounts, unmanaged remote access paths, weak MFA, or privileged activity that hits closer to home.
Hunting requires sustained focus, which is nearly impossible when constant alerts and interruptions force teams into a culture of responding, not exploring. In practice, this shows up when teams start a hunt but don’t finish it. A hypothesis is defined, initial data is pulled, and then a spike in alerts pulls the analyst back into triage. The hunt is paused, context is lost, and by the time they return to it, the thread is gone. Most hunts don’t fail because the idea was wrong, they fail because there isn’t enough uninterrupted time to follow through.
Threat Hunting Is Constrained by Time, Not Intent
By Katrina Thompson
Alert triage dominates analyst time in most SOCs, with analysts spending more than half their time on false alerts, and that’s not counting the oft-cited 67% that get ignored due to lack of time.
Poor or Reactive Hypotheses Undermine Hunts
Within security operations, threat hunting strengthens overall detection and response capabilities by identifying gaps that automated controls may miss. Findings from hunts are often fed back into the SOC in the form of new detections, improved alert logic, and refined response procedures, helping security teams become more effective over time.
In this context, an AI SOC normalizes data, contextualizes alerts, and assembles hypotheses across tools, taking on the role of the retrieval and correlation layer. Analysts aren’t building the dataset anymore, they’re analyzing it.
Ironically, this culture of reactivity bleeds over until it obfuscates what’s truly dangerous to the environment. Hunts are often driven by external triggers like (advisories, peer breaches, news articles) rather than internal knowledge of coverage gaps. This means teams chase what’s topical rather than what’s most likely to evade detection in their own environment.
AI offsets not only the workload, but the cost of execution for threat hunts. Instead of analysts manually gathering identity data, endpoint telemetry, etc., and aligning them into a cohesive timeline, AI autonomously gathers telemetries together and scans for patterns across low-level signals an analyst wouldn’t recognize.
Where AI Changes the Equation
However, that doesn’t negate the fact that AI can help, and potentially in a big way, when SOCs know how to apply it to the process.
Now, 62% of attackers use LOTL techniques in their attacks. This means the majority of modern incidents are undetectable by traditional detection tools, leaving organizations to rely heavily on proactive methods like threat hunting.
While AI is an obvious force-multiplier, at no point does it replace humans in threat hunts. AI serves up the information, but analysts are still required to ask the questions and interpret the results. What changes with AI in the equation isn’t who owns strategy or responsibility (humans), but who owns the work: AI.
What This Means for Threat Hunting
AI-assisted threat gives organizations the ability, both technically and financially, to keep threat hunts running around the clock. This has become imperative as more exploits (AI-crafted, in particular) are being designed especially to bypass traditional detection tools.
Case in point: Verizon reported that “30% of data breaches now involve third parties”, double the prior year.
Executing hunts faster means that teams are no longer forced to treat them as occasional projects that require carved-out time on the calendar. They can become part of normal daily operations. Analysts can test more hypotheses, revisit past assumptions, and check whether new attacker methods could bypass existing controls.
The bottleneck is, as always, how fast humans can make decisions regarding the threat data at their disposal. AI will never replace human judgement, but it can provide the technical might to get them to the relevant data faster.
It is not for lack of ideas or curiosity that threat hunting can be difficult to sustain; rather, the structure of most SOC environments does not leave enough uninterrupted time for strategic work, much less strategic work at scale. When analysts spend most of their time working through alert queues, the environment is rarely conducive to hypothesis-driven investigation.
Hunts don’t happen because they don’t ring alarm bells. Teams are typically more preoccupied with what’s visible externally because they lack the bandwidth to A, even do that completely, and B, proactively look for things that are exposed on the inside.
