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AI Governance & Trust: Building Accountability into Intelligent Systems
Designed for executive briefings and IT consulting presentations
Slide Deck Outline
AI Governance & Trust: Building Accountability into Intelligent Systems
Designed for executive briefings and IT consulting presentations (10–12 slides)
Presented by:
Coeus Digitech Integrations (CDI)
Akoni S. Vaughans Sr., CSM, CSPO
Date:
October 29, 2025
Why Governance Matters
AI adoption is outpacing control frameworks
Risks: bias, misuse, model drift, compliance violations
Trust = License to Operate in the AI economy
Statistics: % of enterprises reporting AI risk incidents ↑ since 2023
The Shift in AI Governance
From Innovation Focus
↓
To Value & Risk Balance
Governance moves upstream in the AI lifecycle
Assistive AI
Supporting human decisions
Augmented AI
Enhancing capabilities
Agentic AI
Independent action
Diagram: 3-stage evolution (Assistive AI → Augmented AI → Agentic AI)
Core Principles of Responsible AI
AI Governance Framework
Four-Layer Model (visual flow diagram):
01
Policy & Ethics Board
Defines principles and accountability
02
Model Governance Layer
Documents owners, risks, metrics
03
Risk Controls & Monitoring
Audits, bias tests, drift tracking
04
Feedback & Retraining Loop
Continuous learning and compliance updates
Alignment with Global Standards
NIST AI RMF 1.0
Risk taxonomy and mitigation
ISO/IEC 42001
AI Management Systems
OECD AI Principles
Fairness & Accountability
Diagram showing governance mapped to each standard
Lifecycle Integration
Visual: AI lifecycle wheel
Ideation
Design
Development
Deployment
Monitoring
Governance checkpoints per stage
Example controls:
  • Data quality review
  • Ethics approval
  • Drift alert system
Consulting Approach for Clients
Step 1: Readiness Assessment
Identify current AI use and risks
Step 2: Framework Design
Governance policy, roles, controls
Step 3: Implementation
Integrate into ML pipelines and tools
Step 4: Monitoring & Training
Bias testing, audit automation, staff enablement
Step 5: Continuous Improvement
Metrics and feedback loops
Metrics & Performance Indicators
% Models with documented risk owners
% Bias incidents resolved within SLA
Model accuracy variance by demographic
Governance audit compliance rate
Trust Index (qualitative user survey score)
Common Challenges & Mitigation
Consulting Leader's Role
Assess governance maturity
Design AI policy frameworks
Embed compliance into pipelines
Train teams on responsible AI practices
Communicate value of trust as a business asset
"Trust is the foundation of intelligent transformation."
Closing & Call to Action
Next Steps:
  1. Schedule a Governance Workshop (2-hour executive session)
  1. Request CDI's AI Readiness Assessment Template