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Security Engineer

Conduct threat hunting

Enhances✓ Available Now

What You Do Today

Beyond waiting for alerts, you proactively search for signs of compromise — analyzing network traffic, endpoint behavior, and authentication logs for indicators that something bypassed your defenses.

AI That Applies

AI identifies subtle behavioral anomalies across endpoints and network traffic that rule-based detection misses, surfacing potential threats for investigation.

Technologies

AI Threat HuntingUEBANetwork Traffic Analysis

How It Works

The system monitors network traffic, access logs, and threat intelligence feeds in real time. The processing layer applies the appropriate analytical models to the structured data, generating scored outputs that surface the most actionable insights. The results integrate into the practitioner's existing workflow — presenting recommendations, flags, or automated outputs alongside their normal working context.

What Changes

Threat hunting becomes more targeted when AI surfaces behavioral anomalies worth investigating rather than you querying raw logs.

What Stays

Forming hypotheses about attacker behavior, understanding adversary tradecraft, and the intuition that says 'this doesn't look right.'

What To Do Next

This section won't tell you what your numbers should be. It will show you how to find them yourself. Every instruction below produces a real, verifiable result in your organization. No benchmarks, no projections — just the steps to build your own evidence.

1

Establish Your Baseline

Know where you are before you move

Before adopting AI tools for conduct threat hunting, understand your current state.

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Map your current process: Document how conduct threat hunting works today — who does what, how long it takes, where the bottlenecks are. You need this baseline to measure improvement.
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Identify the judgment points: Forming hypotheses about attacker behavior, understanding adversary tradecraft, and the intuition that says 'this doesn't look right. These are the boundaries AI won't cross.
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Assess your data readiness: AI tools for this area need data to work. Check whether your organization has the historical data, integrations, and data quality to support AI Threat Hunting tools.

Without a baseline, you can't measure whether AI actually improved anything. You'll adopt tools without knowing if they're working.

2

Define Your Measures

What to track and how to calculate it

Time per cycle

How to calculate

Measure how long conduct threat hunting takes end-to-end today, then after AI adoption.

Why it matters

The most visible improvement is speed. If AI doesn't save time, question whether it's adding value.

Quality of output

How to calculate

Track error rates, rework frequency, or stakeholder satisfaction scores before and after.

Why it matters

Speed without quality is just faster mistakes. Measure both.

When to check: Check after 30 days of consistent use, then quarterly.
The commitment: Give new tools at least 30 days before judging. The first week is always awkward.
What NOT to measure: Don't measure AI adoption rate as a KPI. Adoption follows value — if the tool helps, people use it.
3

Start These Conversations

Who to talk to and what to ask

your engineering manager or VP Eng

“What would have to be true about our data quality for AI to work reliably in conduct threat hunting?”

They're deciding which AI developer tools to adopt team-wide

your DevOps or platform team lead

“What's our current capability gap in conduct threat hunting — and is it a people problem, a tools problem, or a process problem?”

They manage the infrastructure that AI tools depend on

4

Check Your Prerequisites

Confirm readiness before you invest

Check items as you confirm them.

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