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Privacy and Security by Design for AI: Explainability and Robustness
This episode of the ISACA Advanced in AI Audit (AAIA) exam prep series examines how risk mitigation for AI begins inside the architecture itself, long before a system is deployed. You’ll see why nondeterministic models demand a proactive design philosophy, how established privacy and security frameworks apply, and why explainability and robustness are central audit concerns. The discussion equips auditors to judge whether a system was built with controls baked in from the start or bolted on as an afterthought.
What this episode covers
- Nondeterministic AI — why models that may answer differently each time demand a design-first approach to controls.
- Privacy by Design — the seven foundational elements that embed privacy into architecture from day one.
- The ICO seven privacy principles — the UK regulator’s adaptation, including data minimization and user rights.
- Secure by Design — defense in depth, secure-by-default products, and the three guiding manufacturer principles.
- Explainability — why high-dimensional matrices create opacity and how documentation satisfies GDPR and the EU AI Act.
- Robustness — input validation, throttling, error handling, safe failure modes, and defending new attack surfaces.
- Change management for AI updates — informing stakeholders, controlled processes, and the importance of rollback plans.
Watch the full episode above for the worked examples and detailed explanations of each concept.
Frequently Asked Questions
What are the seven elements of Privacy by Design?
The seven elements are being proactive not reactive and preventative not remedial, privacy as the default setting, privacy embedded into design, full functionality meaning positive-sum not zero-sum, end-to-end security across the full life cycle, visibility and transparency, and respect for user privacy that keeps the user at the center.
What is Secure by Design and defense in depth?
Secure by Design weaves cybersecurity into a product from the first day of development and relies on defense in depth, which uses multiple overlapping layers of security so an attacker who breaches the front door still faces a motion sensor, alarm, and steel safe. It includes being secure by default, meaning common threat defenses ship turned on without charging extra, and rests on three principles: the manufacturer owns customer security outcomes, embraces radical transparency, and builds dedicated security structures and leadership roles.
Why is explainability hard for modern AI systems?
With typical software a programmer can read source code, but modern models are trained on billions of parameters that self-learn patterns and encode them into high-dimensional matrices. Because the model builds this web of logic independently, humans struggle to explain why it made a specific decision, yet regulations like GDPR and the EU AI Act still require an explanation, so organizations must rely on detailed documentation techniques.
How do you make an AI system robust?
Robustness means the application keeps functioning safely under unexpected data, changing environments, or attacks, and that if it fails it fails safely. Key techniques include strict input validation and sanitization to mitigate prompt injection, limits and throttling during inference to prevent crashes, error handling with backup plans, and automatic shutdown to a safe mode when accuracy, precision, recall, or safety metrics drop too low.
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Reference: This article is based on concepts discussed in Privacy & Security by Design for AI: Explainability & Robustness.