Governance · Controls · Assurance

A focused engagement to make your AI initiatives safe, compliant, and enterprise-ready. We design and operationalize the policies, controls, and assurance mechanisms that protect sensitive data and reduce AI risk—without slowing delivery.

• Organizations handling PII/PHI/financial data in AI systems
   
• Teams deploying LLMs, RAG, or ML services across regulated domains
   
• Leaders who need clear accountability, controls, and audit-ready evidence
   

• Enterprise AI policy & control framework mapped to your regulations and risk appetite
   
• Data protection by design for AI pipelines (collection → training → inference)
   
• Risk-based model classification with go/no-go criteria and approval gates
   
• Operational guardrails: privacy, security, and responsible-AI checks in CI/CD
   
• Audit-ready evidence pack for internal/external assessors
   

1. AI Privacy, Security & Compliance Framework
    
   • Roles, decision rights, and AI governance charter
      
   • Control library covering data minimization, consent, retention, access, encryption, key management, audit logging, and vendor/third-party AI
      

1. Regulatory Mapping & AIA/DPIA Kit
    
   • Mappings to common regimes (e.g., GDPR/DSAR, CCPA/CPRA, HIPAA/GLBA/PCI as applicable)
      
   • Templates and workflows for AI Impact Assessments (AIA) & Data Protection Impact Assessments (DPIA)
      

1. AI System Inventory & Risk Classification
    
   • Register of models/LLMs, datasets, prompts, connectors, and data transfers
      
   • Risk tiers (minimal → high) with required controls and approval paths
      

1. Secure AI SDLC & Gate Reviews
    
   • Threat modeling (including LLM-specific risks: prompt injection, data exfiltration, jailbreaks)
      
   • Pre-prod and pre-release gates: privacy tests, security scans, evals, red-team results, rollback plan
      

1. Data Safeguards for AI
    
   • Patterns for RAG and fine-tuning with PII/PHI (segmentation, retrieval scoping, masking/pseudonymization)
      
   • Privacy-preserving techniques (tokenization, hashing, k-anonymity, differential privacy—where appropriate)
      

1. Evaluation & Monitoring Pack
    
   • Safety/evasion tests (toxicity, bias, PII leakage), reproducible eval harness, model/data cards
      
   • Runtime monitoring: access, drift, leakage signals, incident runbooks, and SLAs/SLOs
      

1. Training & Playbooks
    
   • Short courses for engineers, analysts, and business users
      
   • Playbooks for prompt hygiene, data handling, and incident response
      

Week 1: Rapid Discovery & Risk Baseline
Stakeholder workshops; current-state scan of data flows, models, vendors; gap analysis vs. target controls.

Week 2–3: Design & Tailoring
Draft the control framework, regulatory mappings, AIA/DPIA templates; define risk tiers and approval gates; design secure AI SDLC.

Week 4–5: Embed & Pilot
Implement priority guardrails in one or two AI products (catalog integration, DLP rules, secrets/KMS, policy-as-code, eval harness); run first AIA/DPIA.

Week 6: Readout & Mobilize
Finalize artifacts, adoption plan, operating model (RACI, councils), and a 90-day rollout roadmap.

(Timelines can compress/expand based on scope and availability.)

• Data governance: ownership, classification, lineage, retention, DSAR/subject-rights workflows
   
• Access & identity: least privilege, privileged access, service accounts, human-in-the-loop approvals
   
• Security controls: network isolation, VPC/private endpoints, secrets mgmt, KMS/HSM, code signing, container/image security
   
• Model/LLM safeguards: content filtering, grounding policies, output watermarking options, prompt injection defenses, safe-completion rules
   
• Supply chain: vendor/OSS risk, model marketplace intake, third-party DPAs, subprocessors
   
• Audit & assurance: evidence collection, dashboards, continuous control monitoring
   

• 100% of AI systems registered and risk-classified
   
• 95%+ of high-risk AI changes pass privacy/security gates before release
   
• ≥ 99% PII masking coverage on Tier-1 datasets used by AI
   
• Time to fulfill data-subject requests involving AI ↓ 50%
   
• Zero critical leakage incidents across production AI services
   

• Access to current policies, data maps, platform diagrams, and vendor lists
   
• Sponsors from Security, Privacy/Legal, and Data/AI product teams
   
• Read-only visibility into pipelines, model registries, and monitoring tools
   

• Shadow AI & tool sprawl: enforce intake, registration, and minimum controls
   
• Data leakage via LLMs: retrieval scoping, red-teaming, output filters, and secrets hygiene
   
• Compliance surprises: AIA/DPIA built into delivery, not after the fact
   
• Over-governance: smallest-viable controls aligned to value and delivery speed
   

• Hands-on remediation for top gaps (DLP, tokenization, access re-architecture)
   
• Continuous assurance service (control monitoring, quarterly attestations)
   
• Independent red-team exercises for LLM/RAG and guardrail tuning
   
• Vendor selection and contracting support for catalog, lineage, DLP, and KMS
   

We blend enterprise security, privacy law awareness, and practical AI delivery. You get guardrails that engineers actually adopt—and evidence auditors can trust.

Share your current AI use cases, primary data types (PII/PHI/PCI), and cloud/tool stack. We’ll schedule a 30-minute scoping call and tailor the engagement to your risk profile and regulatory landscape.