Most threat hunting discussions focus on the big stuff: confirmed intrusions, critical-severity alerts, known-bad indicators. But in practice, the signals that matter early in an attack are small, ambiguous, and spread across multiple tools.
Correlating those signals manually is slow enough that most teams never get to it. AI-assisted hunting is starting to change what’s feasible here, particularly around correlation speed and coverage of low-confidence events.
Manual Pivoting Is Where Hunts Stall
Anyone who has run a threat hunt manually knows the friction. You’re pivoting across your SIEM, identity provider, EDR console, and network tools, writing queries in different syntaxes, normalizing timestamps and field names, and trying to hold a mental model of the investigation together across all of it.
That’s before you even get to hypothesis formation. Following an open-ended lead (“Where else has this IP appeared?” or “What process spawned this binary?”) means querying multiple consoles, normalizing results, and manually stitching timelines together. As Prophet Security has noted, each pivot costs minutes. Across a full hunt, those minutes compound.
Pattern recognition adds another layer. Recognizing that a spike in failed logins, an unusual PowerShell execution, and a suspicious outbound connection might be related requires experience that many SOC teams are short on. Junior analysts, who increasingly carry the load, often lack the reps to spot those connections reliably.
The result is predictable: hunts get scoped down to what’s tractable. High-confidence signals get investigated. Lower-confidence ones, the kind that often matter early in a campaign, get triaged out.
Low-Confidence Signals Are Often the Only Evidence Attackers Leave
Sophisticated adversaries design their operations to produce exactly the kind of signals that busy teams deprioritize. The early stages of the SolarWinds compromise illustrate this well. Before anyone recognized the campaign, the observable evidence was a routine software update, service account creation that mimicked legitimate naming conventions, authentication from IPs that weren’t on any blocklist, and irregular outbound connections to attacker-controlled infrastructure. Individually, none of these would have triggered escalation in a typical SOC. Together, they told the story.
This is the core tension in threat hunting: the signals that matter often don’t look like they matter, and the manual effort required to correlate them across domains means they rarely get the attention they need.
How AI Agents Change the Correlation Problem
An AI agent can pull authentication logs, endpoint telemetry, email metadata, and cloud API activity into a single attack graph, surfacing sequences that look benign in isolation but suspicious in aggregate.
Rather than requiring an analyst to manually query each data source and stitch the results together, the agent handles the data retrieval and normalization steps autonomously.
This changes the practical scope of a hunt in a few ways. First, the agent can follow investigative threads that an analyst would deprioritize under time pressure. A login from an orphaned account, a minor policy exception, a DNS query to a domain registered last week: these are collected and correlated, even if none alone would justify a manual investigation.
Second, the agent can surface context that helps junior analysts make better decisions about where to focus next. AI SOC platforms can use historical baselines and cross-domain context to suggest next investigative steps, reducing the gap between what a senior hunter would pursue and what a less experienced analyst actually does.
Third, every step the agent takes is logged. This matters for auditability, but it also means SOC managers can review the reasoning chain and calibrate trust in the output over time.
Continuous Coverage Changes the Math
The feasibility of manual threat hunting is bounded by analyst availability. Most SOCs concentrate their hunting during business hours with their senior people, which means overnight and weekend coverage is limited to reactive alert triage at best.
AI-assisted hunting doesn’t solve the staffing problem, but it does change the coverage math. An agent that continuously correlates low-level events can surface patterns that develop over hours or days, the kind that would span multiple analyst shifts and likely lose coherence in handoffs.
In a human-led, AI-enabled SOC model, the analyst still drives the investigation for significant incidents, but the AI maintains investigative continuity across the gaps. For teams that can’t justify the cost of full overnight staffing (which is most teams), this is where the operational impact is clearest.
The Bar Is Moving
As attack tooling gets more sophisticated at generating low-signal intrusion patterns, the ability to correlate weak indicators across domains is shifting from a nice-to-have to a baseline expectation.
Teams that rely on manual processes alone will increasingly find themselves investigating only the signals attackers intended them to see.
Article written by: Katrina Thompson
An ardent believer in personal data privacy and the technology behind it, Katrina Thompson is a freelance writer leaning into encryption, data privacy legislation, and the intersection of information technology and human rights. She has written for Bora, Venafi, Tripwire, and many other sites.