Al Cybersecurity Threat Detection
This consultancy work package is designed for organizations that have moved beyond basic AI experimentation and are now facing the "Cyber Arms Race" of 2026. As adversaries transition from human-operated attacks to Autonomous AI Agents, your defense must pivot to high-speed, automated detection.
Objective: Identify the specific "AI-on-AI" attack vectors relevant to your infrastructure using the MITRE (Adversarial Threat Landscape for AI Systems) framework.
Adversarial Profiling: Map your AI stack against 2026 TTPs, including AI Service API Exploitation (AML.T0096) and AI Agent Clickbait (AML.T0100).
Shadow AI Discovery: Use automated scanning to find unmanaged LLM integrations and "Ghost Agents" operating outside of security oversight.
Prompt Injection Simulation: Perform red-teaming for both Direct and Indirect Prompt Injection, focusing on how external data (emails, web searches) can hijack your internal agents.
Deliverable: Custom AI Threat Landscape Report & MITRE ATLAS Heatmap.
Objective: Centralize AI telemetry into your existing security glass-pane (e.g., Microsoft Sentinel, Splunk, or CrowdStrike).
Log Normalization: Standardize logs from diverse AI sources (OpenAI APIs, local Llama instances, LangChain orchestrators) into a unified schema.
Automated Response (SOAR): Build playbooks for "AI Containment," such as:
Trigger: Detection of AI Recommendation Poisoning (AML.T0080).
Action: Immediate session reset and automated clearing of the agent’s "short-term memory" (context window).
Cross-Layer Correlation: Link endpoint alerts with AI API anomalies to catch attackers using AI as a "living off the land" tool for lateral movement.
Objective: Move from periodic audits to "Always-On" defensive testing.
Autonomous Red Teaming: Deploy AI-driven breach and attack simulation (BAS) tools that constantly probe your models for weaknesses.
Model Bill of Materials (MBOM) Monitoring: Scan your AI supply chain for "poisoned" open-source models or compromised third-party plugins.
Drift & Accuracy Monitoring: Monitor for "Adversarial Perturbations"—subtle input changes that cause your security models to misclassify malware as safe.
AI Detection Strategy: A technical blueprint for integrating AI telemetry into your SIEM/XDR.
MITRE ATLAS Playbook: A set of 20+ detection rules and response scripts tailored to AI-specific attacks.
Resilience Assessment: A report on your AI’s "break-point"—the level of adversarial pressure required to cause model failure.
AI SOC Readiness Training: Workshops for analysts on how to investigate "Agentic" incidents.