16-2: Platform Merge Economics
Running two platforms is expensive. Merging them is expensive. Here's how to calculate which path costs more.
🎯 What You'll Learn
- ✓ Calculate dual-stack costs
- ✓ Model merge timelines
- ✓ Project consolidation savings
- ✓ Design the merge roadmap
AI Governance & Compliance Audit: Establishing Security Frontiers
Track 16 — Executive Playbooks & Guides | Module Code: 16-2
The proliferation of Large Language Models (LLMs) fundamentally alters enterprise security postures and compliance landscapes. Unsecured LLM integration introduces novel attack vectors, exposing critical data and jeopardizing regulatory adherence. This playbook provides an executive and technical framework for establishing robust AI governance and an auditable compliance perimeter, specifically addressing the unique challenges posed by stochastic AI functions in regulated environments. Strict adherence to these directives is non-negotiable for maintaining competitive advantage and regulatory standing.
Key Takeaways
1. Mapping the EU AI Act to Your Software Architecture
The EU AI Act mandates a risk-based approach, categorizing AI systems by their potential impact. For regulated enterprises, most LLM deployments will fall into "high-risk" or "limited risk" categories, necessitating rigorous compliance by design.
- Risk Classification Integration: Embed an AI risk registry directly into your architectural review process. Each LLM usage instance (e.g., RAG, summarization, code generation) must be formally assessed against high-risk criteria (e.g., critical infrastructure, employment, law enforcement, credit scoring). This assessment must precede any deployment into production or staging environments processing PII/PHI.
- Data Governance & Provenance: Establish clear data lineage for all training data, fine-tuning datasets, and real-time inference inputs. Implement metadata tagging to track consent, usage restrictions, and original source. Architect data pipelines with immutable logs for auditing. This directly addresses Article 10 (data governance) and Article 13 (transparency and human oversight).
- Robustness & Accuracy By Design: Integrate adversarial testing frameworks into your CI/CD pipelines for AI. Implement mechanisms for model versioning, performance drift detection, and automated retraining triggers. Architect for explainability where feasible, providing model cards detailing biases, limitations, and intended use cases, fulfilling Article 13 and 14 requirements.
- Human Oversight & Intervention: Design human-in-the-loop interfaces for critical decisions made or assisted by AI. This includes clear override mechanisms, confidence score flagging, and a structured process for human review of high-impact AI outputs. This directly supports Article 14.
2. Implementing Data Loss Prevention (DLP) for Copilots
Enterprise Copilots, by design, aggregate information across diverse internal systems. Without robust controls, this aggregation becomes a critical data egress vector. Standard network DLP is insufficient; context-aware, real-time data inspection is mandatory.
- API Gateway Integration: All outbound API calls to external LLM providers must traverse a dedicated AI DLP gateway. This gateway operates as a mandatory inspection point, irrespective of application-layer controls.
- Real-time PII/PHI Redaction: Implement sub-second latency PII/PHI detection and redaction capabilities at the gateway. This must include Named Entity Recognition (NER) for common entities (names, addresses, account numbers, medical codes) and pattern matching for specific enterprise data types (e.g., internal project codes, proprietary financial identifiers).
- Contextual Blocking & Alerting: Configure the DLP gateway to not only redact but also block transmissions that exceed predefined sensitivity thresholds or attempt to exfiltrate specific highly confidential data patterns. Integrate blocking events with SIEM for immediate incident response.
- Tokenization for Inference: Explore and implement tokenization or format-preserving encryption for sensitive data elements where full redaction would impair model utility. The original sensitive data never leaves the perimeter.
3. Audit Trails for Stochastic Functions
The non-deterministic nature of LLMs (stochastic functions) complicates traditional audit trails. A prompt to an LLM does not guarantee a singular, repeatable output, challenging forensic analysis and compliance verification.
- Comprehensive Input/Output Logging: Log every prompt (user input), the exact model version used, all relevant RAG source documents (if applicable), all model parameters (e.g., temperature, top_p, seed), and the complete model response. This logging must be immutable and timestamped.
- Confidence Scoring & Rationale Logging: Where possible, integrate confidence scores or explainability rationales (e.g., LIME, SHAP) into the audit trail, especially for critical decisions. Log any human overrides or modifications to AI-generated content.
- Chain of Custody for AI-Augmented Decisions: For any decision or output that is AI-augmented and subsequently acted upon, establish a clear chain of custody documenting the AI's role, human review, and final human approval. This is critical for demonstrating human oversight.
- Periodic Review & Reconciliation: Implement automated processes to cross-reference AI-generated outputs against business outcomes, flagging discrepancies for human review. This proactive auditing helps identify model drift or emergent biases.
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Module Syllabus
Lesson 1: Part 1: The AI Attack Surface
Interactive Module Section.
Lesson 2: Part 2: Implementing Strict PII Scrubbing
Interactive Module Section.