AI Threat Detection | AI Security Monitoring

Real-time AI threat detection and monitoring — detecting prompt injection, jailbreak attempts, data exfiltration via LLMs, anomalous AI usage, and AI-assisted cyberattacks.

AI Threat Detection — Monitor AI Systems for Attacks

Real-time detection of attacks against AI systems and AI-assisted attack techniques targeting your organisation — unified in one security operations console with MITRE ATLAS framework coverage.

LLM interaction log ingestion with cross-signal correlation in the unified SOC console
Prompt injection and jailbreak attempt detection rules for AI-layer threats
LLM output exfiltration indicators — sensitive data pattern detection in model responses
AI-assisted attack detection: AI-generated phishing, deepfake signals, automated exploitation
MITRE ATLAS adversary tactic coverage for AI-specific attack techniques
AI-specific incident response playbooks integrated into the alert workflow

Two Converging Detection Challenges

Attacks on AI systems (prompt injection, model abuse) and attacks using AI as a tool (AI phishing, deepfakes) require different detection techniques — Threatstealth addresses both in one console.

AI Attacks Are Invisible to Traditional SIEM

Prompt injection and model manipulation leave no signatures in network or endpoint logs. AI-layer visibility requires specialised detection rules applied to LLM interaction data.

AI-Assisted Attacks Are Scaling Volume and Quality

AI tools enable attackers to generate highly personalised phishing at volume and automate vulnerability exploitation — detection capabilities must be updated to match.