Generative AI Governance and Brand Safety Training Course

Participants can identify legal and policy requirements affecting generated content decisions.

• EU AI Act obligations distinguished from voluntary governance commitments
• Privacy impact assessments address personal data in prompts and outputs
• Copyright provenance checked for training data, inputs and generated assets
• Consumer protection rules tested against synthetic endorsements and misleading claims
• Records retention schedules cover prompts, approvals, outputs and incident evidence
• Jurisdictional counsel escalations triggered by regulated claims and cross-border deployment

Participants can assess generative AI content risks and select proportionate controls.

• NIST AI RMF functions translated into generative content control activities
• Risk registers score likelihood, impact, detectability, velocity and control effectiveness
• Scenario analysis traces harms from prompt to audience exposure
• Risk thresholds define approval gates, prohibited uses and escalation routes
• Control libraries distinguish preventive, detective, corrective and compensating measures
• Residual risk acceptance documented with accountable executive sign-off

Participants can translate brand standards into practical generative AI content guardrails.

• Brand safety taxonomy separates unsuitable, illegal, harmful and off-brand content
• Tone-of-voice guardrails convert brand principles into generation constraints
• Claims substantiation matrices pair marketing statements with approved evidence
• Sensitive-topic policies define treatment of health, finance, politics and children
• Prohibited-content lists cover hate, harassment, self-harm, sexual and violent material
• Human review thresholds vary by audience, channel, claim and risk score

Participants can establish an operating model for governing internal and supplier-provided AI content services.

• AI governance operating model defines committees, mandates, meeting cadence and decisions
• Three-lines model allocates challenge, assurance and management responsibilities
• ISO/IEC 42001 clauses mapped to governance policies and documented controls
• Model and content inventories record owners, use cases and dependencies
• Vendor due diligence examines training data, safeguards, retention and subcontractors
• Exception management records rationale, compensating controls, expiry dates and approvals

Participants can specify technical and procedural controls for safer AI-generated outputs.

• Prompt templates constrain tasks, sources, tone, prohibited claims and disclosures
• System instructions separate immutable policy controls from user-requested content
• Retrieval source allowlists reduce unsupported statements and outdated information
• Output filters combine keyword rules, classifiers, semantic checks and human review
• Red-team prompts probe jailbreaks, indirect injection, toxicity and impersonation
• Version controls preserve approved prompts, policies, models and test results

Participants can design review and publication workflows that preserve accountability and content evidence.

• Editorial workflows define author, reviewer, approver and publisher accountabilities
• Risk-based approval routes match content categories to qualified reviewers
• Disclosure labels identify AI assistance, synthetic media and material alterations
• C2PA Content Credentials examined for provenance assertions and verification limits
• Publication checklists confirm factual review, rights clearance and brand approval
• Archive records retain source evidence, reviewer comments and released versions

Participants can monitor control performance and investigate generative AI content failures.

• Quality metrics track factuality, citation accuracy, toxicity, bias and brand alignment
• Sampling plans set review rates by volume, consequence and observed error
• Dashboards distinguish leading control indicators from lagging harm indicators
• Incident taxonomy classifies misinformation, rights breaches, privacy exposure and unsafe outputs
• Root-cause analysis uses five whys, fault trees and evidence timelines
• Corrective action plans assign owners, deadlines, verification and closure criteria

Participants can coordinate containment, escalation and communications following an AI content incident.

• Incident triage assesses severity, reach, reversibility, legal exposure and customer impact
• Containment playbooks pause generation, withdraw assets and preserve audit evidence
• Communications matrices coordinate legal, compliance, marketing, security and executives
• Regulator and customer notifications follow defined thresholds, timing and approval routes
• Post-incident reviews separate immediate remediation from systemic control improvements
• Tabletop scenarios test deepfake spokesperson, unsafe advice and campaign failures

Participants can prioritise and communicate a practical generative AI content governance implementation plan.

• Use-case intake forms capture purpose, audience, data, claims and risk tier
• Control baselines prioritize high-risk content, external publication and sensitive audiences
• Ninety-day roadmap sequences policy, inventory, pilot, monitoring and assurance milestones
• Training matrices map creators, reviewers, managers and executives to responsibilities
• Executive reporting packs present risk posture, incidents, controls and decisions
• Personal application plans identify stakeholders, first actions and measurable outcomes