This consultancy work package focuses on the strategic and structural governance of AI. As of 2026, the industry has transitioned from "General AI Guidelines" to the NIST AI RMF Profile for Cybersecurity and ISO/IEC 42001 (AIMS).
This program moves your organization toward a Governance-first AI Architecture, ensuring that autonomous agents and enterprise models operate within verifiable security boundaries.
Objective: To design and operationalize a comprehensive AI Security Management System (AIMS) that integrates technical controls with regulatory compliance across all business units.
Before technical architecture, we align your AI operations with global standards.
NIST AI RMF & ISO 42001 Mapping: Benchmark current AI projects against the four core functions of the NIST AI Risk Management Framework: Govern, Map, Measure, and Manage.
Regulatory Compliance Readiness: Ensure alignment with the EU AI Act (2026 updates), specifically focusing on "High-Risk" classification and transparency requirements for foundational models.
AI Governance Steering Committee: Establish a cross-functional board (Security, Legal, Data Science) to oversee model risk and define the organization's AI Risk Tolerance.
Deliverable: AI Security Maturity Assessment & Regulatory Gap Report.
Moving away from ad-hoc deployments toward standardized, repeatable, and secure blueprints.
Pattern 1: The "Secure AI Gateway": A centralized proxy architecture for all LLM/Agent traffic. This pattern enforces a single point for prompt filtering, PII redaction, and rate limiting.
Pattern 2: Orchestrated Multi-Agent Guardrails: For agentic workflows, we implement a "Coordinator-Worker" pattern where a supervisor agent monitors sub-agents for Goal Drift and unauthorized tool calls.
Pattern 3: Hybrid RAG Isolation: Architectural separation of the "Context Layer" (Vector DB) from the "Inference Layer." This pattern ensures data is only retrieved based on verified user identities.
Deliverable: Enterprise AI Architecture Patterns Catalog.
Visibility into what makes up your AI—data, models, and dependencies.
AIBOM Automation: Implement continuous generation of AI Bills of Materials (AIBOMs). This tracks the specific version/checkpoint of every model, the provenance of training datasets, and third-party library dependencies (e.g., LangChain, PyTorch).
Model Provenance & Attestation: Establish a "Model Registry" where only models with verified cryptographic signatures can be deployed into production.
Vendor Risk Management (VRM) for AI: Develop specialized auditing criteria for third-party AI providers (e.g., OpenAI, Anthropic, AWS Bedrock) regarding data retention and training opt-outs.
Deliverable: AIBOM Lifecycle Policy & Automated Inventory System.
Operationalizing the NIST SP 800-218A (Secure Software Development Framework for AI).
Control 1: Cryptographic Agent Identity: Every AI agent must have a non-human identity (NHI) with short-lived, task-specific credentials.
Control 2: Human-on-the-Loop (HOTL) Gates: Define technical "Circuit Breakers" that pause AI actions for human approval based on transaction value or sensitivity.
Control 3: Adversarial Robustness Testing: Integrate "Red Teaming" as a standard control. Models must be periodically tested for Jailbreak resilience and Model Inversion vulnerabilities.
Control 4: Immutable Reasoning Logs: Mandate the logging of the "Chain of Thought" (CoT) to allow for forensic auditing of autonomous decisions.
Deliverable: AI Security Control Catalog & Implementation Playbook.
AI Security Strategy Document: A 3-year roadmap for AI security maturity.
Model Governance Portal: A dashboard showing the risk status, AIBOM, and compliance level of every enterprise model.
AI Incident Response (IR) Supplement: Specialized procedures for handling "Model Hallucination" and "Agentic Hijacking" events.
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