Security teams face huge volumes of alerts, logs and threat reports. AI helps them find what matters and respond faster.
Defensive Uses
- Anomaly detection in network traffic, logins and user behaviour.
- Malware classification from file features and behaviour.
- Phishing detection in emails and URLs.
- Alert triage: prioritising and grouping alerts to reduce fatigue.
- Summarising incidents and threat intelligence with language models.
- Assisting analysts with investigation queries and explanations of logs.
Limitations
- False positives overwhelm teams if thresholds are poorly tuned.
- Attackers adapt to evade detection models.
- Models need representative, up-to-date data about your environment.
- Language-model assistants can make confident mistakes; analysts must verify.
How Attackers Use AI
More convincing phishing messages, faster reconnaissance, voice cloning for social engineering, and help writing malicious code. Defences should assume attackers have these tools.
Securing AI Systems Themselves
AI applications add new attack surfaces: prompt injection, data poisoning, model theft and leakage of sensitive data through outputs. Include them in threat models and security testing.
Practical Advice
Start with well-defined, high-volume problems like phishing triage, measure false positives and analyst time saved, and keep humans in control of response actions.