The introduction of LLM-powered AI SOC platforms is democratising threat hunting by breaking down the technical barriers that have historically limited access to it for senior analysts.

By allowing analysts to translate intent into platform-specific queries using natural, non-technical language, AI eliminates the need for specialised knowledge like Python scripting or proprietary query languages.

Now we know that artificial intelligence can accelerate threat hunting and open it up to a wider set of team members, but exactly how does it achieve this transformation? This article covers exactly how.

Applied to the threat hunting process, AI can:

1. Automate evidence gathering 
2. Suggest where threats can be hunted
3. Translate intent into queries
4. Provide a reasoning layer that wasn’t there before
5. Enable complex, always-on threat hunting

Threat hunting isn’t good enough if it is sporadic, subjective, or based on human timelines: adversaries are attacking at the speed of machines, and AI-enabled ones at that. 

Weaving AI deeply into modern threat hunting practices will now only “speed things up,” but change the threat hunting expectation from an occasional benefit to a constant, standard practice. 

1. Automating Evidence Gathering (& Saving SOC Cycles)

At the start of a threat hunt, one looming barrier stands in the way: gathering evidence. For the typical SOC, this means toggling between a half dozen tools, taking screenshots, and compiling the case.

With AI, security operations automation becomes a reality. As leading AI SOC platform company Prophet Security explains, “Once a hunt starts, [an AI SOC solution] pulls logs, events, and metadata from integrated sources without requiring the analyst to query each one manually.” 

Without the use of AI, this process can take up to an hour with manual investigative querying processes alone: across SIEM, EDR, email, IAM, etc. With AI, that timeline is reduced to less than 20 minutes.  

2. Suggesting Threat Hunts: Getting to What Matters

However, before evidence can even be gathered, analysts need to know what they’re hunting: the hypothesis. 

Not all SOCs are equipped with the same technical expertise or the same amount of time to do a hunt. The status quo is that threat hunting is currently a proactive measure; something done to stay ahead of threats missed by detection rules and done as a hygienic best practice. Otherwise, threat hunting is a strictly reactive procedure as part of the incident response process, and typically done in response to a recent breach or an upcoming audit.

Either way, feeling ahead of the game or behind it still makes threat hunting seem “special.” The end goal is to make it seem standard.

And neither scenario leaves hunters with all that much time to carefully choose where to start, or what to pursue. With so many possible signals, any one of them could lead to a wider issue – or to a dead end. Getting hours into a hunt only to realise the road leads nowhere is a waste of time and money, and every threat hunter knows the feeling.

AI can suggest the threats worth hunting before anyone even starts looking at the signals. By ingesting telemetry from across all integrated tools (EDR, identity logs, network traffic, SIEM), it creates a baseline of normal behaviour. 

When something deviates from normal behavior, it can go one step further by mapping to known attacker techniques (MITRE ATT&CK), and then form a hypothesis about what could be wrong. 

Most importantly, not all hypotheses are created equal. AI knows this. It ranks hypotheses by criticality (asset criticality, privilege level, likelihood) and presents hunters with a ranked list: not a best-guess, intuition-inspired direction. 

Then, all analysts have to do is ask the right questions.

3. Translating Intent into Queries: No Coding Required

Currently, when analysts want to query systems, they have to speak the respective language. With AI, Large Language Models (LLMs) do this technical heavy lifting for threat hunters. In an AI SOC, even a junior analyst can type in a simple request:

“Where else across the environment was this (flagged) IP seen?”

And AI will use natural language processing to translate the plain-language question into platform-specific query languages (SQL, SPL, KQL): no technical interface required. No manual coding. This not only makes “every analyst a threat hunter,” thereby speeding up how many threat hunts can be performed, but it also makes each hunt faster.

Senior analysts can skip the long lines, the reviewing and editing, and the technical learning curves to searches; instead, they can focus on the actual “thinking” part of threat hunting. 

Increasingly, AI is doing even that, too.

4. Providing Additional Reasoning, At Machine Speed

Automation-only tools (SOAR, XDR) may correlate events, but the best AI SOC platforms tell analysts why they happened. Agentic AI is behind that. 

By providing an additional reasoning layer, analysts can move more quickly and confidently through hunts, having a built-in backup “brain” at each step.

Agentic AI constructs dynamic attack narratives, building an attack graph across users, hosts, processes, and network connections. It processes and correlates context, tying it into the broader story. 

After mapping to MITRE ATT&CK, it can show analysts:

1. A timeline of the attack
2. A likely attack path
3. Any missing steps

These missing steps are where threat hunters fill in. It takes teams from raw logs to the structured intent of the attacker, bypassing hours of analysis, toggling, and piecing together clues along the way.

Now, instead of “Suspicious PowerShell execution” alerts, teams get something like: “Suspicious PowerShell on a domain controller by a rarely used admin account after anomalous login.”

Starting there means starting with a significant head start. 

5. Enabling Complex, Always-On Threat Hunting for Max Coverage

Another reason threat hunting with AI is faster than threat hunting without it, is that AI never tires. In traditional setups, humans are the head, foot, and tail of threat hunts. They might operate automated tools, but things don’t happen until they’re at the controls.

While most SOCs run 24/7, small teams and even large enterprises understand how hard (and costly) that can be. Your 3 am threat hunting team is not going to be as sharp, savvy, or awake as your 9 am team.

Or, as AI.

AI-enabled threat hunting through an AI SOC means vigilance that never sleeps, tires, or makes mistakes out of exhaustion. Mental powers are never taxed, and help surface signals that may otherwise be overlooked.

Speed Becomes Consistency

AI makes threat hunting faster. And when things are done faster, they can be done more often.

This benefits large enterprises, who, even at their best, may only conduct threat hunting once a week (or once a day for elite achievers).

This benefits mid-tier organisations that hover somewhere between quarterly threat hunts and even-based threat hunts: trying to stay on top of things but having to split analysts between proactive activities and daily tasks.

And it benefits the smallest companies that struggle to even staff a SOC, much less a SOC full of experienced threat hunters.

For all these teams, AI gives them something they never had: round-the-clock threat hunting, done at machine speed, and proactive security that comes standard. 

The Takeaway: At a time when AI-driven threats never sleep, AI-driven threat hunting is more than a nice recommendation. It is the new norm for organisations that understand AI attackers aren’t playing by traditional detection rules, and that they will increasingly be found only via ongoing, AI-powered threat hunts.