AI in Cybersecurity: Where It Genuinely Helps and Where It Misleads

Alert triage is the clearest win: ranking a queue by likely severity, clustering related alerts into a single incident, and summarising an investigation timeline saves analyst hours without removing human judgement from the decision.

Behavioural anomaly detection also works well for problems that resist signatures — impossible travel, unusual data access volumes, a service account suddenly behaving like a person.

Anomalous is not malicious. Models trained on your environment learn your bad habits as normal, and they degrade quietly as the environment changes. A model that autonomously blocks traffic or disables accounts will eventually disrupt a legitimate business process at the worst possible moment.

Treat model output as evidence with a confidence score, not as a verdict. Keep a human in the loop for any irreversible action.

Deploying AI internally creates its own exposure. Prompt injection through untrusted content, sensitive data pasted into third-party models, over-permissive tool access granted to an agent, and model outputs trusted by downstream systems are all live risks that belong in your threat model.