Your MDR doesn't
see AI threats.
Prompt injection. Model poisoning. Agentic AI autonomy failures. LLM data exfiltration. These don't trigger your SIEM. We assess the gaps your MDR can't cover.
Coverage Analysis
Your MDR's AI Blind Spots
We assess the 6 gaps. Your MDR handles the rest.
The Blind Spots
6 attack vectors your MDR can't see.
Prompt Injection Attacks
Attackers manipulate LLM inputs to bypass controls, extract data, or execute unauthorized actions. Your SIEM sees normal API traffic.
OWASP LLM01Training Data Poisoning
Compromised training data degrades model accuracy and introduces backdoors. No signature exists for a poisoned dataset.
OWASP LLM03 · MITRE ATLASLLM Data Exfiltration
Sensitive data leaks through model outputs — PII, credentials, proprietary code. Traditional DLP doesn't inspect LLM responses.
OWASP LLM06Agent Autonomy Failures
Autonomous AI agents exceed their intended scope — making purchases, modifying data, or contacting external systems without authorization.
OWASP Agentic Top 10Shadow AI Proliferation
Employees adopt AI tools without IT knowledge. Every unsanctioned ChatGPT wrapper is an unmonitored data pipeline.
NIST AI RMF · ISO 42001Tool & Plugin Exploitation
AI agents with tool access create new attack surfaces. One compromised tool gives lateral movement through your AI stack.
OWASP Agentic Top 10Coverage Matrix
What's covered. What's not.
We complement your existing MDR — we don't replace it.
Framework Coverage
6 AI security frameworks. Zero covered by traditional MDR.
Your MDR vendor monitors against MITRE ATT&CK — that's their job. These 6 frameworks cover the AI-specific attack surface they don't address.
OWASP LLM Top 10
2025
The 10 most critical LLM security risks — prompt injection, insecure output handling, training data poisoning, model theft, excessive agency.
MITRE ATLAS
Ongoing
Adversarial threat landscape for AI/ML systems. Tactics and techniques from reconnaissance to model evasion.
OWASP Agentic Top 10
Dec 2025
Agent behavior hijacking, tool misuse, identity & privilege abuse. If you deploy autonomous agents, this is your threat model.
NIST AI RMF
2023
Govern, Map, Measure, Manage — the federal framework for AI risk management.
ISO 42001
2023
The international standard for AI management systems. Gap assessments against Annex A controls.
EU AI Act
2025–2027
Risk classification, conformity assessments, transparency requirements. Penalty exposure up to €35M.
How We Work
Four steps to AI security clarity.
AI Security Gap Assessment
We map your AI systems, identify what your MDR can't see, and score your exposure against OWASP LLM Top 10, MITRE ATLAS, and the Agentic Top 10.
10 minutes (self-service) or 2-hour guided session
Quantified Risk Report
Board-ready report with dollar-quantified risk exposure, framework compliance gaps, and prioritized findings.
Delivered within 48 hours of assessment
AI Monitoring Roadmap
A concrete plan for closing the gaps — which tools to deploy, what to monitor, how to integrate AI detection into your existing SOC.
Custom roadmap aligned to your stack
Ongoing Advisory & Review
Quarterly AI security posture reviews, emerging threat briefings, and framework update tracking.
Retainer or per-engagement
The Litmus Test
10 questions to ask your MDR vendor.
If they can't answer "yes" to at least 7, your AI systems have no security coverage.
Does your MDR vendor monitor for prompt injection attacks against your LLM-powered applications?
Can your MDR detect when an AI agent exceeds its authorized autonomy scope or accesses tools outside its permissions?
Does your MDR track shadow AI usage — employees using unsanctioned AI tools that create unmonitored data pipelines?
Can your MDR detect training data poisoning or model drift that degrades your AI system accuracy?
Does your MDR map threats against OWASP LLM Top 10 and MITRE ATLAS, or only MITRE ATT&CK?
Can your MDR identify when sensitive data (PII, credentials, IP) leaks through LLM outputs?
Does your MDR monitor inter-agent communication in multi-agent AI architectures?
Can your MDR provide NIST AI RMF or ISO 42001 compliance reporting for your AI systems?
Does your MDR vendor have a documented methodology for testing AI-specific attack vectors?
Can your MDR generate board-ready AI risk reports that quantify exposure in dollar terms?