AI And Machine Learning

AI and Machine Learning in Security Programs

AI and machine learning matter to security teams because they are now part of how organisations classify events, automate decisions, detect anomalies and support governance. The practical issue is not just what the terms mean, but how they change model risk, operational trust and the controls needed around training data, model behaviour and deployment.

For practitioners, the important distinction is that machine learning is one way AI systems learn patterns from data, while AI is the broader umbrella that includes non-learning systems as well. That distinction affects how you design oversight: an ML model can drift, inherit bias from data, or behave differently after retraining, while a rules-based system fails in more predictable ways.

How Machine Learning Changes the Risk Profile

Machine learning introduces a different failure mode from fixed-rule software because the system’s behaviour depends on the quality, freshness and provenance of the data it learned from. If the training set is incomplete or manipulated, the model can make wrong predictions with high confidence. If the operational environment changes, the model can degrade even when the code has not changed.

That is why AI security is not only about protecting software. It also includes protecting datasets, model files, feature pipelines, prompts where relevant, and the assumptions that surround model performance. A system can be technically functional and still be unsafe if it is overtrusted or used beyond the conditions it was validated for.

Guidance on AI model risk management is especially relevant here because model inventory, validation and monitoring are the mechanisms that keep AI and ML aligned with business use and risk appetite.

Security Controls Around Models, Data and Deployment

The security posture of AI and ML systems depends on controls across the lifecycle, not just at release time. Training data needs provenance and quality checks, models need integrity protection, and deployment environments need access controls, monitoring and segregation so that an attacker or careless operator cannot alter behaviour unnoticed.

This is also where supply chain risk appears. Open-source model components, packages, notebooks and orchestration tooling can become attack paths if they are not curated and verified. In practice, a model pipeline can be compromised long before the model reaches production, which is why provenance and dependency control matter.

For an operational perspective on this lifecycle, see the AI data poisoning and backdoors guide and the AI model security guide, both of which cover how model integrity can fail before or after deployment.

Where AI and Machine Learning Are Commonly Abused

Attackers target AI and ML systems because they often expose valuable data, decision logic or automated action paths. Common abuse patterns include evasion, model extraction, poisoning, backdoor insertion, prompt manipulation in generative systems, and infrastructure abuse around notebooks, APIs and pipelines. The attacker’s goal is often to reduce detection quality, steal the model, or make the system produce outputs that benefit the attacker.

Defenders should treat these as distinct classes of compromise rather than one generic “AI risk”. Evasion changes what the model sees, poisoning changes what the model learns, and extraction changes what the model reveals. Each one requires a different defensive emphasis, from red teaming and monitoring to access control and provenance checks.

The Adversarial Examples and Evasion Attacks Guide and AI Threats and Model Security Guide provide a useful map of those abuse patterns and the detection strategies that go with them.

Practitioner Guidance

Why practitioners should care: AI and machine learning are not just model-building disciplines, they are operational systems whose behaviour changes over time. That means ownership has to include data quality, model validation, deployment controls and post-release monitoring, not only development and testing.

What to watch for: Treat unusual confidence, unexpected drift, unexplained performance changes and weak data provenance as governance signals, not just technical anomalies. Those are often the first signs that an AI or ML system is being misused, degraded or trusted beyond its proven limits.

Practitioner takeaway: The safest AI and ML programmes are the ones that assume models will change, fail and be targeted, then build monitoring and accountability around that reality from the start.

Related resources from NHI Mgmt Group

• What do regulators expect from AI and machine learning risk models?
• How should teams govern AI workflows that span multiple machine learning platforms?
• How should security teams reduce adversarial machine learning risk in production AI systems?
• How do AI and machine learning improve compliance outcomes for DLP programmes?