Extending Your SOC for AI Threats

Most Security Operations Centres were designed for a world of network intrusions, malware payloads, and phishing campaigns. They do that work well. But AI systems introduce a fundamentally different class of threat, one that traditional detection logic, response playbooks, and analyst training simply do not cover.

The question is not whether to rebuild the SOC from scratch. It is how to extend what already works so that AI-specific threats fall within the same operational discipline.

The Gap Between Traditional and AI-Specific Threats

A conventional SOC monitors for known indicators of compromise (IoCs): suspicious IP addresses, file hashes, anomalous login patterns, command-and-control traffic. These remain essential. But AI systems create new attack surfaces that sit outside the scope of traditional telemetry.

Consider the following:

• Model drift occurs when an AI system’s predictions gradually shift away from its intended behaviour, sometimes through natural data changes, sometimes through deliberate poisoning. No firewall alert will catch this.
• Prompt injection allows attackers to manipulate large language models into bypassing safety controls or leaking sensitive data. These attacks leave no signature that a SIEM rule would recognise.
• Training data poisoning can compromise a model before it ever reaches production, embedding biases or backdoors that activate only under specific conditions.
• Model theft and extraction attacks probe an API to reconstruct proprietary models, often staying well within normal usage thresholds.

Each of these threats demands new detection strategies, new telemetry sources, and new response procedures.

Extending Detection: AI-Specific Indicators of Compromise

The first step is defining what “suspicious” looks like for AI systems. Traditional IoCs focus on network and endpoint behaviour. AI-specific IoCs require monitoring at the model and data layer.

Model Behaviour Monitoring

Establish baselines for model performance: accuracy, confidence distributions, output distributions, and latency. Significant deviations from these baselines can signal drift, poisoning, or adversarial manipulation. This is not a one-off exercise. Baselines must be recalibrated as models are retrained and as data distributions change naturally.

Data Pipeline Integrity

Monitor the integrity of training and inference data pipelines. Unexpected changes in data volume, schema alterations, or anomalous data sources should trigger investigation. If an attacker can tamper with training data, they can control model behaviour without ever touching the model itself.

API and Query Pattern Analysis

For models exposed through APIs, track query patterns for signs of extraction attacks. Techniques like model stealing often involve systematic probing with carefully crafted inputs. Unusually structured queries, high-volume access from single sources, or patterns that resemble known extraction methodologies should raise alerts.

Prompt and Output Logging

For generative AI systems, log prompts and outputs (with appropriate privacy controls) to detect injection attempts, jailbreaking, and unintended data leakage. This telemetry is new territory for most SOC teams, and it requires careful handling to balance security monitoring with data protection obligations.

Updating Response Playbooks

Detection is only half the equation. When an AI-specific alert fires, analysts need clear procedures for investigation and containment.

Model Quarantine Procedures

Define what it means to “quarantine” a compromised model. This might involve rolling back to a known-good version, restricting the model’s access to production data, or temporarily replacing it with a simpler fallback system. The key is having these options documented and tested before an incident occurs.

Cross-Functional Escalation

AI incidents rarely stay within the SOC’s usual remit. A poisoned model might require data science expertise to diagnose. A prompt injection incident might have legal and regulatory implications. Response playbooks must include escalation paths to ML engineering teams, data governance functions, and legal counsel.

Evidence Preservation

Forensic practices for AI incidents are still maturing. At a minimum, preserve model weights, training data snapshots, inference logs, and pipeline configurations at the time of detection. These artefacts are the equivalent of disk images and memory dumps in traditional incident response.

Tabletop Exercises for AI Threats

The AI security roadmap emphasises preparedness as a core principle, and tabletop exercises are one of the most effective ways to build it. Yet most organisations run exercises that focus exclusively on traditional scenarios: ransomware, data breaches, insider threats.

AI-specific tabletop exercises should explore scenarios such as:

• Scenario 1: Model poisoning discovery. A routine performance review reveals that a credit-scoring model has developed a systematic bias. Investigation suggests deliberate data manipulation. How does the team respond? Who makes the decision to take the model offline? What are the regulatory notification requirements?

• Scenario 2: Prompt injection at scale. A customer-facing chatbot begins providing instructions that violate company policy after an attacker discovers a reliable injection technique. The attack is spreading on social media. How quickly can the team contain it? Who handles communications?

• Scenario 3: Supply chain compromise. A widely used open-source ML library is found to contain a backdoor that exfiltrates model weights during training. Multiple internal models may be affected. How does the team assess the blast radius? What is the recovery plan?

These exercises achieve two things. They expose gaps in current procedures, and they build the cross-functional muscle memory that real incidents demand.

Building AI Literacy in the SOC

Extending tools and playbooks is necessary but insufficient without extending analyst capability. SOC analysts need foundational understanding of how AI systems work, not to become data scientists, but to recognise when something is wrong.

Practical steps include:

• Targeted training modules covering AI attack taxonomies (OWASP Machine Learning Security Top 10 is a strong starting point), model lifecycle basics, and AI-specific log interpretation.
• Joint exercises with ML engineering teams, building relationships and shared vocabulary before incidents force collaboration under pressure.
• Dedicated AI monitoring roles within the SOC for organisations with significant AI deployments. These specialists can triage AI-specific alerts and mentor the broader team over time.

Measuring Progress

SOC maturity models typically assess capabilities across detection, response, and prevention. Extend these assessments to include AI-specific dimensions:

• What percentage of AI systems have defined behavioural baselines?
• Are AI-specific IoCs integrated into SIEM and monitoring platforms?
• Do response playbooks exist for the most likely AI threat scenarios?
• When was the last tabletop exercise that included an AI-specific scenario?
• What percentage of SOC analysts have completed AI security training?

These questions provide a practical scorecard for tracking progress.

Start With What You Have

The SOC does not need a complete transformation to begin addressing AI threats. It needs targeted extensions: new telemetry sources, updated playbooks, cross-functional relationships, and analyst upskilling. The operational discipline that makes a SOC effective (triage, escalation, evidence preservation, continuous improvement) applies just as strongly to AI threats as it does to traditional ones.

The organisations that extend their SOC capabilities now, rather than waiting for the first AI-specific incident, will be the ones best positioned to operate safely in an AI-driven environment.