Agentic AI in the SOC: Faster Triage, Fewer Misses

Alert overload isn’t a “process problem.” It’s a math problem. When detection coverage grows faster than analyst capacity, teams start making trade-offs they’d never choose on purpose: sampling alerts, rushing ticket notes, and closing incidents with shaky confidence.

A recent example from Transurban (operator of 22 toll roads across Australia plus roadways in the US and Canada) puts real numbers behind what many SOCs quietly admit: they were triaging only 8% of tickets because the volume was that overwhelming. Their response wasn’t to hire a dozen analysts (expensive, slow, and hard to retain). They built an agentic AI workflow that checks ticket quality and severity in real time—then routes fixes back to humans.

This post is part of our AI in Cybersecurity series, where we focus on practical ways AI supports threat detection, fraud prevention, anomaly detection, and security operations automation. Here’s the stance I’ll take: agentic AI belongs in the SOC—but only when it’s designed as a controlled system, not a magic autopilot.

Agentic AI isn’t “a chatbot for security”—it’s a workflow engine

Agentic AI in cybersecurity is simplest to describe like this: it’s AI that can take a goal (triage, validate, enrich, recommend) and execute a sequence of steps across systems—then hand off decisions to humans or automation based on rules.

That difference matters. Traditional “AI in the SOC” often means one of two things:

• A model that scores alerts (useful, but limited)
• A copilot that answers questions (helpful, but still analyst-driven)

Agentic AI sits between detection and response. It doesn’t just summarize—it traverses context, checks whether required fields and evidence are present, maps events to playbooks, and flags inconsistencies before tickets are closed.

If your security operations are drowning in repeated work—classification, enrichment, ticket hygiene, SLA checks—agentic AI is a direct fit because those tasks are:

• High volume
• Rule-bound (at least partially)
• Expensive when done poorly (missed incidents, bad reporting, audit pain)

This also connects cleanly to fraud prevention and anomaly detection. Fraud teams have lived with “too many alerts” for years. The winning pattern is the same: automate the boring verification steps, keep humans for judgment and edge cases, and measure outcomes relentlessly.

What Transurban built: two agents, one purpose—better tickets and faster triage

Transurban’s approach is worth studying because it’s not trying to replace analysts. It’s trying to stop analysts from wasting time and shipping low-quality outcomes.

They developed an in-house agentic system built on large language models that works alongside their SOC tooling. The structure is refreshingly concrete:

Agent #1: Categorize and score incidents correctly

This agent reviews incident ticket fields and checks categorization—severity, incident type, and other required metadata. In a typical SOC, this is where errors creep in:

• An incident gets mislabeled (hurts reporting and prioritization)
• A severity is inflated (wastes time) or deflated (increases risk)
• Key context is missing (investigation restarts later)

The big operational benefit: you don’t find out at month-end that half your tickets are messy—you find out immediately.

Agent #2: Verify resolution notes before closure

This is the quiet killer in most SOCs: closure notes that don’t match evidence, don’t reference the playbook step that was executed, or omit what auditors will later demand.

Transurban’s second agent checks whether the resolution is consistent with the incident and the expected playbook path. It doesn’t simply accept “resolved” as an answer.

And here’s the guardrail that makes this safer: the agent doesn’t close tickets on its own. It sends a summary back to the analyst, the analyst fixes gaps, then the agent re-verifies before closure.

That pattern—AI proposes, humans approve, AI verifies—is the most durable way I’ve seen to use agentic AI in security operations.

The results: 100% coverage, <3% false positives, 60% faster triage

If you want a clean “why now” argument for agentic AI in cybersecurity, use outcomes like these.

After extensive testing and deployment (reported as rolling out in September), Transurban states:

• 100% coverage of incidents (no more triaging a tiny fraction)
• False-positive rate under 3% for the agent-driven checks
• Triage time reduced by 60%
• Accuracy rate of 92%

Even if your mileage varies, the shape of the benefit is the point:

• You increase coverage (less risk hiding in the backlog)
• You reduce time-to-triage (less dwell time)
• You improve governance quality (better notes, better reporting, better SLA compliance)

This is exactly where AI-driven security operations automation pays off: not in flashy “autonomous hacking defense,” but in consistently executing the work humans are too busy to do perfectly.

Why safety-critical industries are early adopters (and why that should scare you—in a good way)

Transurban’s environment adds urgency: roadway systems that can influence traffic flow. When digital systems affect physical outcomes, organizations tend to be less tolerant of sloppy processes.

A line that sticks: human safety is the top factor. That’s why governance matters. In safety-critical environments, you can’t afford:

• Incidents closed without evidence
• Incomplete investigations
• Untracked SLA misses
• “We’ll fix reporting later” culture

Here’s the uncomfortable truth: most enterprises are also safety-critical; they just don’t admit it. Healthcare, logistics, utilities, financial services, public sector—if your systems go down or get manipulated, people get hurt. The harm might be indirect, delayed, or economic, but it’s real.

Agentic AI fits these environments because it enforces consistency. And consistency is what makes security measurable.

How to implement agentic AI in a SOC without creating a new risk surface

Agentic AI adds power—and power adds blast radius. If you’re evaluating autonomous SOC capabilities, you need a plan that’s operational, not theoretical.

1) Start with “ticket quality” before “auto-response”

Most companies get the order wrong. They chase automated containment before they can reliably produce accurate tickets.

A safer maturity path looks like this:

1. Summarize and enrich (context gathering, mapping to assets)
2. Validate and normalize tickets (fields, severity, categorization)
3. Playbook adherence checks (did we do step 1–5?)
4. Recommended actions with approvals (human gates)
5. Limited automated response (constrained, reversible)

Transurban is clearly in stages 2–3 and moving toward 4–5.

2) Put hard constraints around what the agent can touch

If an agent can call tools, it can do damage. Keep early deployments inside strict boundaries:

• Read-only access to SIEM data where possible
• Write access limited to drafts, comments, or recommended fields
• No ability to disable detections or modify logs
• Action execution behind explicit approvals (at least at first)

If you’re thinking “that sounds slow,” remember the goal: reduce analyst time while increasing confidence. Safety beats speed when you’re building trust.

3) Treat model choice like an integration and governance decision

Transurban selected a model that fit their environment and integrated with their stack (SIEM, ticketing, managed model hosting). That’s practical.

For most SOCs, selection criteria should include:

• Integration with SIEM and case management
• Support for controlled tool-calling and policy enforcement
• Auditability of prompts, outputs, and actions
• Tenant isolation and data handling controls

The “smartest model” is rarely the best choice if you can’t govern it.

4) Measure outcomes that security leadership cares about

If you want buy-in (and budget), track metrics that map to risk reduction and operational efficiency:

• Triage time reduction (minutes per ticket, not vibes)
• Coverage rate (percent of alerts triaged within SLA)
• Reopen rate (tickets reopened due to missing/incorrect resolution)
• False-positive and false-negative rates for the agent’s checks
• Mean time to detect (MTTD) and mean time to respond (MTTR)

Agentic AI is only “working” if these numbers improve—and stay improved.

Where agentic AI goes next: from anomaly detection to contained response

The next phase described by Transurban is the logical one: bring in external threat intelligence and automate more of the triage and response chain. This is also where many AI in cybersecurity programs either mature—or stumble.

External intelligence isn’t helpful unless it’s contextual

Dumping threat intel into the SOC creates noise. The right approach is selective enrichment:

• Only pull intel that matches observed indicators
• Weight intel by relevance to your sector and geography
• Correlate intel with asset criticality (domain controller vs test server)

Agentic AI can do this well because it can follow a sequence:

1. Extract indicators from the alert
2. Query intel sources
3. Compare to your environment and prior incidents
4. Recommend severity adjustments and next actions

That’s anomaly detection with context—exactly what most teams want but don’t have time to do.

Automated response should be narrow, reversible, and logged

When you move from “recommend” to “do,” keep actions constrained:

• Isolate an endpoint for 10 minutes pending analyst review
• Disable a compromised token/session
• Block a known malicious domain temporarily
• Quarantine an email campaign

Make reversibility a design requirement. Make logging mandatory. If you can’t explain what the agent did, you shouldn’t let it do anything.

Practical “People also ask” answers (for SOC leaders)

Is agentic AI the same as an autonomous SOC?

No. Agentic AI is a capability; an autonomous SOC is an operating model. You can deploy agents for quality checks and enrichment without automating containment or remediation.

Will agentic AI reduce headcount?

Sometimes, but that’s the wrong goal. The better goal is higher coverage and better decisions with the same team. In many SOCs, the win is avoiding uncontrolled growth in headcount as alert volume climbs.

Where does agentic AI fit in fraud prevention?

Fraud prevention lives on anomaly detection and rapid verification. Agentic AI helps by:

• Correlating signals across systems (device, identity, transaction)
• Checking policy and playbook steps automatically
• Creating consistent case narratives for investigators

Same pattern, different domain.

A better way to think about agentic AI: “quality at scale”

The strongest signal from the Transurban case study isn’t the model choice. It’s the operating principle: use agentic AI to enforce quality in real time, at the scale your business actually runs.

If your SOC is still triaging a small fraction of alerts—or doing governance checks at the end of the month—agentic AI in cybersecurity is a straightforward fix. Not because it’s trendy, but because it’s built for repetitive work that humans can’t do consistently under pressure.

If you’re evaluating agentic AI for security operations automation, start with a controlled pilot: one queue, one playbook family, measurable outcomes, human gates. Prove you can improve coverage and accuracy without adding risk.

By this time next year, “AI-assisted SOC” will feel normal. The differentiator will be which teams built the guardrails early—and which teams are still arguing about whether the backlog counts as risk.