AI Threat Detection

Detection in AI environments requires new signals: prompt similarity, output entropy, tool-call sequences, and identity behavior. The threat landscape evolves quickly, so detection logic must update at the same cadence as model and agent deployments.

AI threat detection commonly looks for:

• Injection patterns: Direct and indirect prompt-injection signatures and anomalies.
• Jailbreak attempts: Known and emerging techniques against safety policy.
• Data exfiltration: Unusual outbound volumes or sensitive content patterns.
• Behavioral drift: Agents deviating from established baselines.
• Supply chain signals: Indicators tied to compromised models, MCP servers, or skills.

Effective AI threat detection also relies on context: knowing which model is in use, what data is in scope, and which agents are involved. Detection without context produces alerts that security teams cannot operationalize.

Programs that operate AI threat detection well also map detections back to MITRE ATLAS and the OWASP Top 10s, so reporting and response use the same shared vocabulary as the rest of the security org.

How PointGuard AI Helps

PointGuard combines AI Runtime Guardrails with the Agent Governance Mesh to deliver continuous AI threat detection across prompts, responses, agents, and tool calls, with alerts mapped to MITRE ATLAS and the OWASP Top 10s. The result is detection that produces actionable alerts tied directly to assets, users, and policy outcomes rather than raw model-level events.

Learn More

• MITRE ATLAS
• OWASP Top 10 for LLM Applications
• NIST AI 600-1 Generative AI Profile