How AI Powers Smarter Threat Hunting

Threat hunting is still not a consistent part of most security strategies, as taking the time to do threat hunting may not always deliver results, and takes time away from analysts’ regular duties. But with AI, it can be.

Adding AI to the threat hunting mix does a few things:

Makes a complex, time-consuming task simple and automatable
Lets teams make threat hunting a continuous process, not a sporadic event
Turns every analyst into a threat hunter

All in all, when SOCs can turn occasional, reactive threat hunting into ongoing, proactive threat hunting, organizations can finally match attackers who themselves are going 24/7.

Anthropic’s latest model, Mythos, was not released out of fear that bad actors can use it to find zero-day exploits. The truth is, adversaries are already using AI-powered exploits to barrage organizations with constant and repeated attacks. These attacks aren’t only faster and more consistent; fewer are spotted and end up as alerts, as they have the ability to evade detection and hide in plain sight.

While defenders will have an initial head start in leveraging AI to harden their environment and protect their assets, it also means that autonomous threat hunting is increasingly going to be critical to catching AI-based attacks.

What is AI Threat Hunting?

AI threat hunting is using agentic AI agents and AI SOC platforms to do the work of manual threat hunters:

Forming and testing hypotheses
Ingesting and normalizing data
Querying across siloed tools (EDR, network, identity, cloud)
Correlating multi-domain signals
Finding anomalies vs known signatures
Identifying stealth techniques (living-off-the-land)

In a typical threat hunt, these tasks would be done by hand. As Prophet Security, a leading provider of AI SOC solutions, states: “Answering that question means querying multiple consoles, normalizing results, and manually stitching timelines together. The friction often kills the hunt before it starts.”

But AI scales and autonomizes the process, making threat hunts automatic and leaving humans with only decisions while AI agents do the leg work. This consists of checking alerts and correlating them against log files and previous alerts to determine which is requiring further investigation.

Here’s what that means.

No More Human Middleware

When analysts act as “human middleware,” serving as the integration layer connecting all these disparate pieces, it slows the process down. They’re juggling:

Data movement
Translating formats
Correlating signals
Adding context

Switching between interfaces and dashboards, getting up for breaks, making normal human errors, getting distracted, missing clues or failing to see a connection that’s there: all these things lead to lag and inaccuracies.

This results in analysts spending roughly 60-80% of their time assembling data and only about 20-40% threat hunting. AI does the necessary busy work – manual correlation and aggregation processes – in a fraction of the time and lets analysts get to work.

Bridging the Expertise Gap

Experienced threat hunters are hard to come by. Threat hunting is intellectually high-value work and not every organization can afford for analysts and admins to give up time and effort to do it.

Hunters not only have to know the systems inside and out; they have to read the technical tea leaves and understand how pieces fit together, piecing together attack paths as they unfold. It requires:

Analytical thinking
Data querying skills (SQL, SPL, KQL)
Knowledge of attacker techniques (MITRE ATT&CK)
A “gut sense” of where to look next – this comes from years of experience

It’s called an art and a science for good reason.

But many small or resource-strapped companies can barely manage a fully-staffed SOC, much less one with expert threat hunters on the payroll. Where does this leave them?

AI bridges the gap, acting as a plug-and-play solution that uses contextual reasoning to “think” like an analyst:

Threat-informed heuristics catch signals
Agents compare anomalies to baseline behaviors
AI SOCs suggest next steps using natural language processing (NLP)
AI threat hunts mimic experienced analyst workflows

Guided investigations lead even junior analysts through complex, multi-step investigations, and GenAI enables teams to ask questions in plain language and receive answers in the same way.

Fixing the Problem of Scale

Even when expert threat hunters can be found, the problem is that they can’t threat hunt at scale. For every one hypothesis investigated, there are hundreds of other possible ones left on the table.

Up until now, that has just been an accepted industry reality. But with AI agents, petabytes of data can be analyzed at machine speed, so that reality changes to one where threat hunting can be part of the security strategy.

Now every weak signal, every anomaly, every spike in DNS requests gets analyzed. Time and effort are no longer a barrier. Hypotheses are formed and investigated. Events are correlated across telemetries. And analysts have access to the data they need to make final decisions.

Constant – not Sporadic – Threat Hunts

Along with scale is the issue of consistency. Because they are so resource and time-intensive, threat hunts are typically only done when something prompts them:

A major security breach
Suspicious low-level signals
A regular cadence (monthly or weekly)
New TTPs are disclosed
A detection gap was discovered: did anything get through?

AI improves what we’ve come to expect from threat hunting by making it a 24/7, round-the-clock process. Like detection rules that are always on, an AI-driven threat hunting solution turns weak signals (that don’t trigger alerts) into items that will get investigated nonetheless; no stone goes unturned.

This increases the chances of catching malicious activity closer to when it starts, minimizing fallout and stopping exploits before they’ve had too much of a headstart.

Improving Threat Hunts Over Time

Because it involves autonomous AI agents that use machine learning, AI-powered threat hunting improves over time as it continuously “learns” from past successes, analyst input, telemetry patterns, and attack techniques to evolve into a more custom vehicle over time.

Room for growth comes when it separates “known bad” from “needs investigation.” In these cases, analyst feedback on the ambiguities informs the model’s behavior in the future.

That means that the AI-driven threat hunting platform companies deploy today will not be the one they have twelve months down the road: that will be better with the ‘training’ it has been given over time.

Keeping Humans at the Helm

The most important thing that AI brings to threat hunting is peace of mind: because humans can always remain at the helm.

AI is making the manual, repetitive, laborious tasks quicker and easier. This means analysts can get results in seconds, not hours, and not after pouring over hundreds of lines of data. But it also means that it’s their call on what to do with those results.

In a human hybrid AI threat hunting model, the AI does the execution and investigation. The analyst then reviews what the AI has prepared, validates findings, and makes the final call.

As Kevin Curran, IEEE senior member, states, “Agentic AI will increase automation in reconnaissance, enrichment and even suggestion of hypotheses, but human oversight will remain critical for context, legal decisions and complex reasoning.”

Conclusion

Part time threat hunting by analysts in a swivel chair is no match for attackers moving at machine speed.

AI not only improves threat hunting outcomes; it allows those improved outcomes to become part of an ongoing investigative process: reducing dwell times, bridging expertise gaps, and making threat hunting a process that provides more than token protection.

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