This consultancy work package is designed for organizations seeking to transition from "Experimental AI" to a mature, production-grade AI Enterprise Security Architecture (AESA).
As of 2026, the shift in enterprise AI is away from simple chatbots toward Multi-Agent Systems (MAS) and Autonomous Workflows. This work package focuses on building the "Security Control Plane" required to govern these complex interactions, aligning with ISO/IEC 42001, NIST AI RMF 1.0, and the EU AI Act.
Objective: To design a centralized, vendor-agnostic security architecture that provides visibility, control, and risk mitigation across the entire AI lifecycle—from data ingestion to model inference.
Before technical deployment, we establish the legal and organizational "Right to Operate."
AI Asset Discovery & Inventory: Identify all "Shadow AI" (unauthorized SaaS AI) and internal LLM wrappers.
Risk Tiering Framework: Classify AI use cases based on impact (e.g., Low Risk for internal FAQs vs. High Risk for automated financial credit scoring).
Policy-as-Code (PaC) Definition: Translate the EU AI Act and internal compliance requirements into machine-readable guardrails (e.g., using OPA/Rego).
Deliverable: AI Risk Management Strategy & Compliance Roadmap.
AI is only as secure as the data it consumes. This phase secures the "Context Layer."
RAG Security & Vector DB Hardening: Implement row-level security (RLS) within Vector Databases to ensure an agent only retrieves data the user is authorized to see.
Data Lineage & Provenance: Establish an immutable audit trail of what data was used to train or fine-tune models to defend against Data Poisoning.
Sensitive Data Masking: Deploy real-time PII/PHI redaction layers between the enterprise data source and the Model endpoint.
Deliverable: Secure Data Architecture for GenAI (Blueprint).
Establish a centralized "choke point" for all AI traffic to ensure consistent security policy enforcement.
AI Security Gateway (Firewall): Implement a proxy layer to intercept prompts and completions, scanning for Jailbreaks, Prompt Injection, and Insecure Output Handling.
Model Lifecycle Management (ModelOps): Secure the "Model Supply Chain" by scanning base models (HuggingFace, Bedrock, etc.) for vulnerabilities and "sleeper agents."
Adversarial Red Teaming: Conduct simulated attacks focused on model extraction, membership inference, and logic manipulation.
Deliverable: AI Gateway Configuration & Red Team Vulnerability Report.
Managing the shift from human-triggered AI to autonomous agent-to-agent communication.
Non-Human Identity (NHI) Management: Assign unique, short-lived machine identities to every AI agent, removing the need for shared high-privilege API keys.
Tool-Call Scoping: Restrict the "agency" of models. An agent may have the logic to "Delete User," but the Architecture must enforce a secondary Human-on-the-Loop (HOTL) gate.
Reasoning Forensics (Log-to-Logic): Capture the "Chain of Thought" (CoT) in immutable logs to allow investigators to reconstruct why an AI made a specific, high-stakes decision.
Deliverable: Zero Trust Architecture for Autonomous Agents.
AI Security Blueprint: High-level architectural diagrams for hybrid/multi-cloud AI deployments.
Guardrail Library: Pre-configured sets of safety prompts and output filters.
AI Incident Response Playbook: Specific procedures for handling model "hallucination" crises or adversarial injections.
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