From SOC to AI-SOC: Why quiet AI attacks demand new telemetry, playbooks, and metrics

From SOC to AI-SOC: Why security operations must evolve for the AI era

Most teams think their AI is covered: it sits behind firewalls, IAM is in place and cloud controls are green. From an infrastructure view, that sounds fine. From an AI view, it is blind.

Attackers can manipulate a model without breaking availability, tripping auth checks or causing obvious data loss. They degrade decisions quietly. The system looks healthy while outcomes drift in the wrong direction. That is a business problem, not just a security one.

Why the traditional SOC model misses AI compromise

SOC workflows are tuned for breaches, outages and exfiltration. AI attacks target decisions. They look like "model weirdness," so they get routed to data science as tuning issues instead of to security as adversarial behavior.

That mismatch is structural. Traditional SOCs lack an adversary model for AI, the telemetry to see it and the authority to act inside AI pipelines. By the time impact is visible, clean remediation is hard.

The limits of SIEM/XDR for AI incidents

SIEM and XDR correlate endpoints, identities and networks. They do not read feature distributions, inference patterns or semantic manipulation. A model can be online, fast and compliant while being steered into biased, extracted or corrupted outputs.

No amount of perimeter logging exposes a decision-layer failure. This is not another dashboard problem. It is an observation gap.

Use MITRE ATLAS to define the adversary

AI security improves the moment you switch from "Is the model accurate?" to "Is it being manipulated?" MITRE ATLAS gives you tactics, techniques and procedures for AI abuse across data, training, deployment and inference. It turns AI risk into something a SOC can track, test and answer for.

AI-native telemetry: what Ops must instrument

• Data layer: Label drift by segment, unusual source mix, spikes in access to ingestion paths, rare feature value bursts, "too-perfect" samples. Useful for spotting slow poisoning.
• Model layer: Unauthorized retraining, hash changes to artifacts, unexplained divergence between model versions, missing lineage. Treat models like critical assets, not code blobs.
• Inference layer: Repetitive or structured queries, confidence instability, prompt/response patterns that suggest extraction, inversion or evasion. These signals rarely appear in traditional logs.
• Supply chain: Pretrained model provenance, library integrity, feature engineering repos, build pipelines and signing. Track origin and integrity or risk quiet accumulation of compromise.

Make AI risk observable in the SOC

Extend the SOC; don't replace it. Convert AI techniques into detection hypotheses your SIEM/XDR can consume. Then route, triage and respond like any other incident-with AI-specific playbooks.

• Data poisoning → Alert on label/feature drift beyond baselines, anomalous ingestion access, rare-source surges.
• Model extraction → Correlate high-volume, structured inference calls with token spikes, patterned prompts and new API keys.
• Adversarial evasion → Flag output instability, odd confidence swings and mismatches across canary vs. production models.
• Supply chain compromise → Detect unsigned or mismatched artifacts, dependency changes without approvals and pipeline anomalies.

SOAR actions that contain AI incidents

• Throttle or rate-limit inference for suspicious tenants or tokens.
• Roll back to last-known-good model; activate a canary or shadow model for validation.
• Quarantine data pipelines; freeze ingestion from suspect sources.
• Rotate secrets, invalidate access tokens and require re-attestation of model artifacts.
• Snapshot evidence: datasets, prompts, outputs, model hashes and pipeline logs.

30-60-90 day plan for Operations

• Day 0-30: Inventory all AI systems, owners, data sources, models, endpoints and pipelines. Map them to ATLAS techniques. Define what constitutes "AI incident" vs. "model performance issue."
• Day 31-60: Stand up AI-native telemetry at data, model, inference and supply chain layers. Stream signals to SIEM. Add initial rules for drift, extraction patterns and artifact integrity.
• Day 61-90: Build SOAR playbooks (throttle, rollback, quarantine). Run a tabletop and a red-team simulation. Close gaps in logging, approvals and rollback paths.

Metrics leadership will care about

• Asset visibility: % of AI systems with owners, lineage and provenance documented.
• Coverage: % of AI assets mapped to ATLAS techniques with active detections.
• Detection speed (MTTA): Time from adversarial signal to alert.
• Containment effectiveness (MTTR): Time to throttle, isolate or roll back.
• Integrity SLA: Days in compliance for model hash/signing and data source approvals.
• Escape rate: % of AI incidents discovered by the business (bad decisions) vs. by the SOC.

Operating model: who does what

• SOC: Owns detections, correlation, triage and incident command across AI signals.
• Platform/ML Ops: Provides telemetry, enforces approvals, executes rollbacks and pipeline isolation.
• Data Science: Validates adversarial hypotheses, compares model behavior and supports recovery.
• Risk/Governance: Tracks AI-specific risk metrics, regulatory impact and post-incident reviews.

Leadership takeaway

If you deploy AI without evolving your SOC, you will not find compromise in your alerts-you will find it in your outcomes. Adversaries already know how to push models off course quietly and cheaply. Define the playbook (ATLAS), wire the telemetry and make response real.

Want your Ops team fluent in AI risk and response? Explore focused upskilling by role at Complete AI Training.

For broader context on risk governance, see the NIST AI Risk Management Framework.