Detect threats targeting your AI — and threats using AI
Real-time detection of attacks against AI systems (prompt injection, model abuse, data exfiltration) and AI-assisted attack techniques targeting your organisation — unified in one security console.
What is AI Threat Detection — Monitor AI Systems for Attacks?
AI threat detection covers two converging security challenges: detecting attacks targeting AI systems (prompt injection, jailbreaks, model exploitation, data leakage via LLM) and detecting threats that use AI as an attack tool (AI-generated phishing, deepfakes, automated vulnerability scanning, LLM-assisted social engineering). Threatstealth unifies both detection categories in a single security operations console.
The two AI threat detection gaps organisations face
- Attacks on AI systems — prompt injection, jailbreaks, data exfiltration via LLM outputs — are invisible to traditional SIEM rules and intrusion detection signatures
- AI-assisted attacks (AI phishing, deepfakes, automated social engineering) are increasing in volume and effectiveness while detection rates remain low
- SOC teams lack playbooks, detection rules, and tooling specifically designed for AI-layer threats
- AI usage logs are rarely integrated into SIEM — security teams cannot correlate AI events with other security signals
A four-step operational model
AI Usage Log Integration
Ingest LLM interaction logs, model access logs, and AI tool call events into the security monitoring pipeline alongside traditional network and endpoint telemetry.
- LLM interaction log ingestion
- AI tool call event correlation
- Cross-signal timeline building
AI Attack Detection Rules
Apply detection rules specifically designed for AI-layer threats: injection pattern matching, anomalous query detection, data exfiltration indicators in LLM outputs.
- Prompt injection detection rules
- Anomalous query volume alerting
- LLM output exfiltration indicators
AI-Assisted Threat Detection
Identify AI-assisted attacks against your organisation: AI-generated phishing, deepfake content, and LLM-assisted social engineering campaigns.
- AI phishing content indicators
- Deepfake detection signals
- Social engineering pattern recognition
Unified SOC Response
AI threat alerts integrate directly into the security operations console alongside traditional alerts — with AI-specific response playbooks for analyst guidance.
- Unified alert queue with AI threats
- AI-specific response playbooks
- Escalation and investigation workflows
Outcomes for security teams
AI attacks are invisible to traditional SIEM
Prompt injection, model manipulation, and data exfiltration via LLM outputs leave no signatures in network logs or endpoint telemetry — they require AI-layer visibility.
AI-assisted attacks are scaling volume and quality simultaneously
AI tools dramatically increase the speed and personalisation of phishing, social engineering, and automated exploitation — requiring updated detection capabilities.
SOC teams need AI-specific playbooks
Responding to a prompt injection incident or AI data leakage requires different procedures than a network intrusion — detection must be paired with response guidance.
Direct answers
What is AI threat detection?+
AI threat detection covers detecting attacks against AI systems (prompt injection, model abuse) and detecting threats that use AI as a tool (AI phishing, deepfakes, automated exploitation).
What is MITRE ATLAS?+
MITRE ATLAS (Adversarial Threat Landscape for Artificial-Intelligence Systems) is a framework mapping adversary tactics and techniques targeting AI systems — the AI equivalent of MITRE ATT&CK for traditional infrastructure.
How does AI threat detection integrate with a SIEM?+
Threatstealth ingests LLM interaction logs and AI tool call events into the detection pipeline alongside traditional network and endpoint data — enabling cross-signal correlation in the same SOC console.
Can Threatstealth detect AI-generated phishing?+
Yes — the platform applies AI-phishing content indicators and anomalous campaign pattern detection to identify AI-assisted phishing campaigns before they reach end users at scale.