Agentic AI for Threat Triage: Faster SOC Decisions

Security teams aren’t drowning in “alerts.” They’re drowning in decisions. Every noisy detection forces a choice: investigate now, defer, or close—and the cost of being wrong is brutal. That’s why what Transurban’s cyber defense team described at Black Hat Middle East—automating threat triage and scoring—hits a nerve for anyone running a SOC.

Agentic AI is showing up as the missing middle layer between detections and humans: it doesn’t just summarize alerts, it takes action inside guardrails—collecting evidence, correlating signals, scoring risk, and handing analysts a clean, prioritized queue. If you’re tracking this “AI in Cybersecurity” series, this is the practical point where AI-driven threat detection turns into automated security operations.

What follows is a field-ready way to think about agentic AI for threat triage: what it does well, where it fails, and how to deploy it without creating a new class of incidents.

Agentic AI triage is about decisions, not dashboards

Agentic AI improves cyber defense when it reduces time-to-decision and increases decision quality for the first 15 minutes of an incident. That’s the moment when teams either contain a threat early—or let it spread while they argue over whether the alert is real.

Traditional SOAR playbooks help, but they’re often brittle: if the alert format changes or a new log field appears, the automation breaks. Agentic AI shifts the model from “if X then do Y” to “given this situation, gather what’s needed, apply policy, and produce a defensible decision.”

Here’s the simplest, most accurate description I’ve found:

Agentic AI in the SOC is an orchestrator that can plan, fetch evidence, and propose actions—while your rules define what it’s allowed to change.

That’s why automating triage and scoring (as Transurban discussed) is a strong first use case. It’s high-volume, repeatable, and measurable.

What “automated triage and scoring” actually means

In a mature setup, agentic AI does four things before an analyst even opens the ticket:

1. Enriches the alert (identity, endpoint, asset criticality, geo, known-good vs unknown).
2. Correlates related events into a single incident narrative.
3. Scores risk using both technical severity and business context.
4. Recommends next actions (contain, reset creds, block indicator, or monitor).

The outcome is not “more alerts handled.” It’s fewer investigations wasted.

How agentic AI boosts threat detection by improving context

Threat detection is only as good as the context you can attach to it. Most organizations have the raw signals—EDR telemetry, firewall logs, identity events, cloud audit trails—but it’s fragmented. Analysts spend their time copying identifiers across tools to build a coherent picture.

Agentic AI helps by treating an alert as a case to be built, not a string to be matched.

The evidence pack: your new SOC unit of work

A practical pattern is to have the AI produce an evidence pack alongside every high-confidence alert. Think of it as a pre-assembled incident brief.

A strong evidence pack typically includes:

• Who: user, role, group memberships, recent privilege changes
• What: process tree, command line, parent-child relationships, file hashes
• Where: device, subnet/VPC, region, ISP/ASN for external IPs
• When: timeline of key events (first seen → escalation → lateral attempts)
• So what: impact estimate (data accessed, systems touched, criticality)
• Why it matters: mapping to known tactics (credential theft, persistence)

When that pack is consistent, two things happen fast:

• Analysts trust the output because it’s repeatable.
• Leaders can measure improvements because triage becomes comparable across incidents.

Fraud and anomaly detection benefit from the same scoring logic

One underappreciated connection: triage scoring is basically the same engine you want for fraud prevention and anomaly detection.

Example: A suspicious login alert is rarely “high” or “low” risk on its own. Risk depends on context:

• Is the user a finance approver?
• Is the device managed?
• Is the login followed by mailbox rule creation or token creation?
• Is there impossible travel plus new MFA method enrollment?

Agentic AI can gather those signals automatically and output a score that reflects real business risk, not just technical severity.

The best first deployments: semi-autonomous, high-volume triage

If you want leads from an AI-in-cybersecurity initiative, start where ROI is easy to prove. Triage fits because it’s measurable: time saved, backlog reduced, false positives cut, and faster containment.

Here are three deployment patterns that work in large enterprises (and don’t require you to bet the farm).

1) “Triage co-pilot” mode (read-only, high trust-building)

Answer first: Start with an agent that can investigate but can’t change anything.

In this mode, the agent:

• Pulls logs and telemetry across tools
• Correlates signals into a timeline
• Suggests a severity score and recommended actions

Humans still click “contain,” “disable account,” or “block.” This is where you find out whether the AI is consistently helpful or confidently wrong.

2) “Guardrailed auto-close” mode (safe autonomy)

Answer first: The fastest win is auto-closing low-risk alerts with proof.

Most SOCs have a huge tail of repetitive noise—alerts that resolve to known-good behavior. Agentic AI can auto-close when it can attach evidence such as:

• Known corporate software hash + signed binary
• Approved admin tooling on an approved admin workstation
• Scheduled task created by endpoint management

The key is policy: auto-close only when two independent confirmations agree (for example, EDR classification + asset allowlist + change ticket match).

3) “Auto-contain for a narrow class of events” (high value, high caution)

Answer first: Let the agent take action only for incidents where delay is clearly worse than a false positive.

Good candidates:

• Commodity malware on standard endpoints
• Confirmed credential stuffing on public-facing auth
• Endpoint isolation when ransomware indicators are present

Bad candidates (early on):

• Domain controller actions
• Core network changes
• Anything affecting safety-critical operations

If you do nothing else, adopt this rule: The blast radius of an automated action must be smaller than the blast radius of the threat.

How to build threat scoring that doesn’t collapse under edge cases

Threat scoring fails when it’s either too simple (“critical if severity=high”) or too opaque (“the model said 92”). The approach that sticks is hybrid scoring: deterministic where it must be, probabilistic where it helps.

A practical scoring model (that your team will actually use)

Answer first: Combine technical confidence, business impact, and attacker progress.

A defensible scoring rubric often looks like this:

• Confidence (0–5): How likely is this to be malicious based on evidence?
• Exposure (0–5): Is the asset internet-facing? Is lateral movement possible?
• Business impact (0–5): What happens if this system/account is compromised?
• Attacker progress (0–5): Recon → initial access → execution → persistence → exfil

Then calculate a final score and map to actions:

• 0–6: Auto-close or monitor
• 7–12: Queue for analyst review
• 13–16: Escalate and prepare containment
• 17–20: Immediate containment playbook

Agentic AI’s job is to fill in the inputs reliably and explain them in plain language.

What the AI must explain every time

If you want analysts to trust the scoring, require these three explanations in every ticket:

1. Top evidence: the 3 facts that most strongly support the score
2. Top uncertainty: what would change the decision (missing log source, blind spot)
3. Recommended next step: one action, not five

The “top uncertainty” line sounds small, but it prevents a common failure mode: the agent pretending the world is cleaner than it is.

Risks you can’t ignore: agent sprawl, prompt injection, and quiet failure

Agentic AI can help, but it also creates new operational risks. If you treat it like a chatbot bolted onto the SOC, you’ll regret it.

Prompt injection is a SOC problem, not a novelty

Answer first: Any agent that reads attacker-controlled text must be treated as a security boundary.

Attackers can plant instructions in:

• Email bodies
• Web content captured in logs
• Ticket descriptions
• Filenames and command lines

Guardrails that work:

• Strip or neutralize untrusted text before the model sees it
• Use tool-based execution (API calls) instead of free-form “do stuff” prompts
• Require signed approvals for actions above a risk threshold
• Log every tool call and every decision input

Quiet failure is the real danger

When automation breaks, it often fails loudly. When agentic AI fails, it can fail quietly—producing plausible narratives that are wrong.

Two controls I strongly recommend:

• Canary incidents: known test cases that run daily to confirm behavior
• Drift reviews: monthly checks for scoring shifts (what changed and why)

If you can’t measure drift, you can’t operate this safely.

A 30-day plan to pilot agentic AI triage (without chaos)

Answer first: Start narrow, instrument everything, and make the agent earn autonomy.

Here’s a realistic month-one rollout plan I’ve seen work.

Week 1: Pick one alert family and define “done”

Choose a high-volume category like:

• suspicious login / impossible travel
• malware/PUA detections
• risky OAuth app / token anomalies

Define success metrics:

• median time to triage decision
• % alerts auto-enriched with complete evidence packs
• false positive rate by severity bucket
• analyst satisfaction (simple 1–5 per ticket)

Week 2: Build the evidence pack and scoring rubric

Make the evidence pack a template. Lock it. Consistency beats creativity.

Then implement hybrid scoring and require explanations (“top evidence / top uncertainty / next step”).

Week 3: Run in co-pilot mode and compare to humans

Have the agent score in parallel with analysts. Track mismatches and categorize them:

• missing context (tool integration gaps)
• logic errors (bad rule weight)
• model error (hallucinated causality)

Week 4: Turn on safe automation for low-risk outcomes

Start with:

• auto-close with evidence
• auto-route to the right queue
• auto-escalate when business impact is high

Save auto-containment for phase two, once trust and auditability are in place.

Where this fits in the AI in Cybersecurity series

Agentic AI for threat triage is the connective tissue between AI-driven threat detection and real operational outcomes: shorter dwell time, fewer missed incidents, and less analyst burnout. Transurban’s focus on automating triaging and scoring is a signal that enterprises are moving past demos and into production habits.

If you’re evaluating agentic AI for a SOC, don’t ask whether it can “analyze alerts.” Ask whether it can produce repeatable evidence packs, defensible threat scoring, and guardrailed actions that stand up during a post-incident review.

If that’s the direction you’re heading, the next step is straightforward: pick one alert family, define your scoring rubric, and run a 30-day parallel pilot. The question that decides whether you’ll win is simple—what decisions are you willing to let the machine make, and what proof do you require each time?