Webinar Description
Key Takeaways
- Examines why privacy frameworks designed for GDPR and CCPA fall short when applied to AI-specific risks
- Addresses practical approaches to auditing and updating existing privacy programmes for AI governance
- Covers vendor risk management challenges as AI capabilities expand across technology stacks
- Designed for CPOs, CISOs, privacy consultants, legal teams and compliance professionals
- Explores regulatory developments including AI-specific disclosure requirements and automated decision-making rules
Introduction
The AI Governance Audit: Is Your Privacy Program Built for How AI Risk Actually Works? is a webinar designed for privacy, security and compliance professionals navigating the intersection of artificial intelligence and data protection. Hosted by DataGrail with contributions from Red Clover Advisors and Cognate Cyber, the session addresses a fundamental challenge facing organisations today: privacy compliance strategies built for earlier regulatory frameworks are proving inadequate for the distinct risk profiles introduced by AI technologies.
This timing reflects broader industry pressures. As AI adoption accelerates across enterprise technology stacks, regulatory bodies worldwide have responded with new legislation targeting automated decision-making, algorithmic transparency and the processing of sensitive data. Privacy teams that once operated with established playbooks now face compliance demands that extend well beyond traditional data subject rights and consent management.
The Gap Between Traditional Privacy Management and AI Governance
Compliance frameworks developed in response to GDPR and CCPA established foundational practices for data inventory, consent management and individual rights fulfilment. These frameworks assumed relatively predictable data flows and processing purposes. AI systems introduce complications that strain these assumptions: models trained on historical data may perpetuate biases, automated decisions can affect individuals in ways that are difficult to explain, and the boundaries between data controller and processor responsibilities become less clear when AI subprocessors are involved.
The webinar examines how AI risk manifests differently from conventional privacy risk. Where traditional compliance focused on data minimisation and purpose limitation, AI governance must also account for model behaviour, training data provenance and the downstream effects of algorithmic outputs. Privacy professionals are increasingly expected to understand not just what data is collected, but how that data influences automated systems that make consequential decisions.
Auditing Privacy Programmes for AI-Era Requirements
A central theme of the session involves practical approaches to assessing whether existing privacy programmes can accommodate AI-related obligations. This includes evaluating current data inventories for AI touchpoints, reviewing vendor agreements for AI subprocessor disclosures, and identifying gaps in policies that predate widespread AI deployment.
The audit process extends beyond documentation. Effective AI governance requires understanding where AI capabilities exist within an organisation’s technology stack, including tools that may have introduced machine learning features through routine updates. Many organisations discover AI processing occurring in systems they had not previously classified as AI-related, creating compliance blind spots that traditional privacy assessments would not detect.
Vendor Risk Management in an AI-Enabled Landscape
As software vendors integrate AI capabilities into their platforms, organisations face expanded due diligence requirements. The webinar addresses how privacy teams can adapt vendor risk management practices to account for AI-specific concerns, including transparency about model training, data retention for AI purposes and the use of customer data to improve AI systems.
Regulatory developments in multiple jurisdictions now require disclosure of AI subprocessors, creating new contractual and operational obligations. Privacy professionals must work with procurement and legal teams to ensure vendor agreements address these requirements before AI-enabled services are deployed.
Building Effective Training and Policy for AI Risk
Technical controls alone cannot address AI governance challenges. The session explores how organisations can develop training programmes and internal policies that promote safer employee behaviour when interacting with AI systems. This includes guidance on appropriate use of generative AI tools, recognition of sensitive data categories that require additional safeguards, and escalation procedures when AI outputs raise concerns.
Updating policy language represents only part of the solution. Effective training must help employees understand why AI systems present different risks than conventional software, enabling them to make informed decisions in situations that policies cannot anticipate.
Who Should Attend
The webinar is suited for professionals responsible for privacy compliance, information security and data governance within organisations subject to privacy regulations. Chief Privacy Officers and Chief Information Security Officers will find relevance in the strategic discussion of programme adaptation, while privacy consultants and legal teams may benefit from the practical audit frameworks presented. Security professionals involved in vendor assessments and technology procurement will gain insight into emerging AI-specific due diligence requirements.