Artificial Intelligence Security Controls /Framework
This consultancy work package focuses on the governance, risk management, and technical security controls required to secure AI systems in a rapidly evolving regulatory landscape. In 2026, organizations must move beyond "AI ethics" and implement rigorous, auditable security frameworks to meet the requirements of the **EU AI Act**, **ISO/IEC 42001**, and **NIST AI RMF 1.2**.
Phase 1: AI Governance & Regulatory Mapping
Objective: Align AI initiatives with global standards and ensure legal compliance.
ISO/IEC 42001 (AIMS) Readiness: Conduct a gap analysis against the International Standard for AI Management Systems.
EU AI Act Classification: Categorize AI systems into risk tiers (Prohibited, High-Risk, Limited, or Minimal) and map specific obligations.
Policy Development: Draft an AI Acceptable Use Policy (AUP) and a Responsible AI Standard covering bias, transparency, and human oversight.
Phase 2: AI Risk Management (NIST & MITRE)
Objective: Operationalize a risk-based approach to AI security.
NIST AI RMF 1.2 Implementation: Apply the **Govern, Map, Measure, and Manage** functions to all high-impact models.
Threat Modeling (MITRE ATLAS): Map your environment against the **Adversarial Threat Landscape for AI Systems (ATLAS) to identify specific TTPs (Tactics, Techniques, and Procedures) such as:
AML.T0098:** AI Agent Tool Credential Harvesting.
AML.T0099:** AI Agent Tool Data Poisoning.
Algorithmic Impact Assessment (AIA): Evaluate the potential societal and individual harms of automated decision-making.
Phase 3: Technical Security Controls (OWASP LLM)
Objective:Harden the AI technical stack against modern vulnerabilities.
The 2026 AI Control Matrix
Control Area | Framework Reference | Implementation Action
Prompt Injection | OWASP LLM01:2025 | Implement semantic firewalls and system-prompt isolation.
Supply Chain| OWASP LLM03:2025 | Create an **AI Model Bill of Materials (MBOM)** for all base models/plugins.
Excessive Agency| OWASP LLM06:2025 | Restrict agentic tool invocation to "Human-in-the-Loop" for critical actions.
Data Poisoning | EU AI Act Art. 15 | Verify training and RAG data integrity using cryptographic hashing.
Phase 4: Monitoring & Incident Response
Objective: Ensure continuous reliability and detection of "Goal Drift."
AI Red Teaming: Conduct "Purple Team" exercises focusing on jailbreaking, model extraction, and indirect prompt injection.
Observability & Logging: Implement specialized logging for LLM tokens, reasoning paths, and tool calls to meet EU AI Act Article 12 requirements.
AI Kill-Switch Protocol : Define automated triggers to disable an AI service if it exceeds safety thresholds or displays non-deterministic harmful behavior.
Engagement Deliverables
1. AI Security Maturity Report:** A scorecard against NIST AI RMF and ISO/IEC 42001.
2. Compliance Matrix:** A direct mapping of your controls to the EU AI Act articles.
3. Threat Model Report:** A MITRE ATLAS-based assessment of your specific AI architecture.
4. AI Incident Response Playbook:** Specific procedures for handling model compromises and data leakage.
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