‍By: Kevin Gonzalez, VP of Security, Operations, and Data at Anvilogic

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I used to think the biggest threat to a Security Operations Center was ransomware. Turns out, it’s nostalgia.

For years we’ve romanticized the old ways: hand-carved detections, caffeine-fueled threat hunts, and heroic analysts triaging alerts with nothing but terminal windows and sheer force of will. Those practices built careers—and terrific war stories—but they also built bottlenecks. Fast-forward to today, and every efficiency initiative, every dash of AI-powered “magic,” threatens to rewrite the roles we once held sacred. Cue the collective panic: Are we automating ourselves out of relevance?

Spoiler: no. We’re automating ourselves out of drudgery.

This article is a reality check (with a healthy side of dark humor) on where detection engineering, hunting, and triage actually stand—and where they’re headed next. We’ll mourn the pain points, spotlight the AI-driven upgrades, and show how each discipline mutates into something leaner, smarter, and frankly more fun.

• Part 1 tackles Detection Engineering’s leap from wrench-turning to governance.
  
  
• Part 2 reimagines Threat Hunting as the SOC’s data-science brain.
  
  
• Part 3 transforms Triage from gut-feel roulette into explainable intelligence.
  
  

If you’re clinging to the past, consider this your intervention. If you’re already knee-deep in LLM prompts and CI pipelines, think of it as validation that you’re not alone on this wild ride.

Either way, buckle up—the SOC is hitting its mid-life crisis, and the upgrade package is anything but boring.

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Part 1 – Detection Engineering: From Grease-Monkeys to Governance Architects

“Detection engineering shouldn’t have to exist; cars don’t need mechanics on the dashboard.”
— Someone on LinkedIn who has clearly never owned a 2002 Jeep

1. Why this discipline refuses to die

Detection logic is still the heartbeat of every SOC. When the 2025 State of Detection Engineering Report surveyed 300+ security teams, 81 % said custom detections are now more important than vendor rules, and 82 % blamed testing for their biggest time-wasting bottlenecks. If “detections should just work,” why is leadership doubling down on the people who build them? Simple:

• Adversaries don’t do factory presets. They change payloads, TTPs, and infrastructure faster than any off-the-shelf feed can ship an update.
  
  
• Environments are snowflakes. What screams “critical” in one cloud tenant is background noise in another. The mirrors need adjusting.
  
  
• Regulators discovered acronyms. PCI DSS 4.0, DORA, TSA—pick your poison. Each wants proof you can detect before you respond.
  
  

So yes, detection engineering lives on—just not in the form many of us cut our teeth on.

2. Classic pain points (a.k.a. the seven stages of carpal-tunnel)

1. Threat research – reading yet another Volt Typhoon teardown at 2 a.m.
   
   
2. Lab re-creation – spinning up infra to confirm “yes, it really does spawn whoami 47 times.”
   
   
3. Logic authoring – regex roulette meets data-model jigsaw puzzle.
   
   
4. Enrichment plumbing – stitching CTI, asset tags, and “that Excel sheet Bob owns.”
   
   
5. Unit testing – six meetings to find one pcap because the malware lab is on a change freeze.
   
   
6. Documentation – explaining all the above to auditors who still think “Sigma” is a fraternity.
   
   
7. Forever maintenance – because Microsoft just renamed the event again.
   
   

Multiply that by hundreds of detections and you’ve got the SOC equivalent of pushing a boulder uphill… every quarter.

3. Enter AI, but keep your seatbelt on

The last two years dumped an LLM-shaped toolbox on our workbenches. Cisco Talos’ 2024 white-paper found that pairing a detection engineer with a domain-tuned LLM would drastically reduce rule-writing time, and that was with GPT 3.5. Gartner’s Market Guide echoes the productivity uptick, crediting security data pipelines that codify AI into version-controlled playbooks as a top cost-saver for 2025.

What the hype often misses is where humans still fit. Modern detection engineering looks like this:

The engineer’s role flips from wrench-turner to governance architect—reviewing AI output, setting testing thresholds, and deciding what good enough looks like.

4. Detection-as-Code or bust

With agents churning out rules at Formula 1 speed, version control isn’t a “nice to have.” Detection-as-Code (DaC) treats every analytic like software:

• Git-native PRs with peer review and regression tests
  
  
• CI checks against replay data to catch the “>” that nukes your index
  
  
• Semantic versioning so downstream hunters know what changed
  
  
• Automated changelogs for auditors and junior analysts alike
  
  

Digitalisation World’s 2025 AI Insights nail the benefit: DaC “bring[s] agility to SOC operations, enabling real-time, validated detections and highly adaptable strategies”.

5. Risks & reality checks

AI pipelines are only as good as the guardrails you weld around them. Watch for:

• Hallucinated fields – Your LLM happily references cmd.exe on a Linux box.
  
  
• Bias toward popularity – If everyone hunts PowerShell, oddball Mac attacks get ignored.
  
  
• Data-quality drift – Log schema changes silently break lookups unless CI catches them.
  
  
• Compliance gaps – Privacy regs may frown on shoving raw user data into a public LLM endpoint.
  
  

Governance architects (a.k.a. old-school detection engineers with new shoulder pads) own these checks. They throttle prompts, gatekeep model updates, and ensure outputs hit precision/recall targets before anything touches prod.

6. Success metrics that matter in 2025

Forget “number of rules shipped.” Measure the stuff the board cares about:

When those numbers trend in the right direction, leadership notices—and suddenly “detection engineering” is no longer the cost center some LinkedIn sages claim.

7. The road ahead

Google Cloud called RSAC 2025 the “dawn of agentic AI,” where autonomous agents shoulder routine SOC tasks and free humans for deep-work investigations. The detection factory is ground zero for that shift:

1. Agents generate candidate rules from fresh CTI drops.
   
   
2. CI bots replay against golden datasets for sanity checks.
   
   
3. Human architects apply the “so what?” filter.
   
   
4. Validation agents kick rules into canary namespaces.
   
   
5. Feedback loops flow back via learning-to-rank, prepping the next iteration.
   
   

The future isn’t “detections magically work.” It’s detections evolve continuously, with AI spinning the wrench and humans steering the car.

Part 2 – Threat Hunting: From Swash-Buckling Explorers to the SOC’s Data-Science Brain

“Hunting is what you do when you didn’t detect it the first time.”
— every seasoned analyst, probably

1. Why the hunt still matters

SANS’s 2024 Threat-Hunting Survey found that 50 % of hunters rely on “their own research” to stay ahead of attacker trade-craft—second only to vendor blogs and CTI feeds (59 %). In other words, new behaviour still slips past factory rules, and someone has to go spelunking for it.

2. Classic growing pains

• Random-walk hypotheses – “Let’s grep for cmd.exe /c whoami again—something crazy might show up this time.”
  
  
• Data-lake hangovers – Ten billion events, five different schemas, one over-caffeinated analyst.
  
  
• No feedback loop – Great ideas vanish into Slack threads because nobody productises them.
  
  
• ROI headaches – Execs love the idea of hunting; they hate “we found two interesting pings” on the QBR slide.
  
  

3. The plot twist: hunting is your data-science arm

Instead of pouring humans into raw telemetry, modern programs layer data-science algorithms on top of an enriched alert lake:

Gartner’s 2025 roadmap for an AI-driven SOC calls this “continuous validation of AI outputs via analyst feedback”—hunters become model-tuning coaches, not dashboard tourists.

4. Outputs that feed the factory

1. Coverage validation – Map alert clusters back to MITRE, spot technique gaps.
   
   
2. Precision boosts – Learning-to-Rank (LTR) models score which alerts deserve rule tuning.
   
   
3. Net-new behaviours – When ML finds truly novel patterns, hunters curate them into new detections.
   
   
4. Threat intel cross-overs – Automatic tagging links discoveries to live CTI, closing the loop in hours, not weeks.
   
   

5. Success signals for 2025

6. Caution tape

• Model bias – If the ML only sees Windows, it won’t notice that weird macOS launchd abuse.
  
  
• “Cool query syndrome” – Hunters must still prove business impact, not just novelty.
  
  
• Data-quality rot – Bad schemas yield nonsense clusters—keep CI on the feature store.
  
  

Bottom line: hunting isn’t dying; it’s graduating from log spelunker to alert-scientist, fuelling precision across the entire SOC pipeline.

Part 3 – Triage: From Gut-Feel Roulette to Explainable Intelligence

“If every alert looks guilty, none of them are.”
— an analyst after a 3 a.m. shift

1. Why change was inevitable

IBM’s 2024 Cost-of-a-Data-Breach report pegs the average breach lifecycle at 324 days for orgs without security AI or automation—and 247 days when they are fully deployed. That 77-day delta is pure triage efficiency.

Meanwhile, AI-powered triage is going mainstream: By the end of 2025, half of SOCs will use AI to sanity-check alerts before a human ever sees them. The spreadsheet era is officially over.

2. The legacy pain parade

• Queue fatigue – Alerts pile up, SLAs slip, morale tanks.
  
  
• Context scrambling – Ten different consoles for IP whois, asset tags, and “is this server even ours?”
  
  
• Experience gap – Junior hires drown; seniors gate-keep tribal knowledge.
  
  
• No provenance – Analysts can’t explain why a rule fired, which torpedoes trust with IR and audit.
  
  

3. The new playbook: explainability first

A modern alert lands with:

Forrester’s 2025 AI predictions stress marrying data and AI strategies to drive exactly this kind of business-to-tech handshake.

4. What analysts actually do now

1. Validate confidence – “Does the 0.92 score pass our threshold for CFO email accounts?”
   
   
2. Add human nuance – Note merger activity, VIP travel, or other off-book factors.
   
   
3. Escalate or close – One-click pushes full provenance to IR; no swivel-chair required.
   
   
4. Feedback loop – Tag false-positives/negatives to retrain LTR and tuning models.
   
   

5. Metrics that move the board

Cost avoidance – breach lifecycle days saved × industry breach-day cost ($15–$20 k/day, per IBM)

6. Things that can still go sideways

• LLM hallucinations – Always link every narrative sentence to raw fields.
  
  
• Over-automation – A mis-tuned confidence threshold can DoS the IR queue.
  
  
• Skills gap – AI doesn’t replace expertise; it accelerates the shortage. IBM pegs that gap at adding USD 1.76 million to breach costs when left unchecked (ibm.com).
  
  

The Through-Line

Detection engineers became governance architects. Threat Hunters levelled up into data scientists. Triage analysts morphed into decision accelerators. Stitch those layers together and you get an AI-amplified SOC capable of shrinking breach lifecycles, cutting analyst toil, and finally delivering the ROI that keeps CISOs out of the board’s hot seat.

Next stop? Agentic AI that writes the QBR slides for us— but let’s get this pipeline humming first.