Tips for Understanding GDPR Compliance

The Belgium Data Protection Agency (DPA) published a report explaining the intersection between the GDPR and the AI Act and how organizations can align AI systems with data protection principles. The report emphasizes transparency, accountability, and fairness in AI, particularly for high-risk AI systems. The report also outlines how human oversight and technical measures can ensure compliant and ethical AI use. AI systems are defined based on the AI Act as machine-based systems that can operate autonomously and adapt based on data input. Examples in the report: spam filters, streaming service recommendation engines, and AI-powered medical imaging. GDPR & AI Act Requirements: The report explains how both frameworks complement each other: 1) GDPR focuses on lawful processing, fairness, and transparency. GDPR principles like purpose limitation and data minimization apply to AI systems which collect and process personal data. The report stresses that AI systems must use accurate, up-to-date data to prevent discrimination or unfair decision-making, aligning with GDPR’s emphasis on data accuracy. 2) AI Act adds prohibitions for high-risk systems, like social scoring and facial recognition. It also stresses bias mitigation in AI decisions and emphasizes transparency. * * * Specific comparisons: Automated Decision-Making: While the GDPR allows individuals to challenge fully automated decisions, the AI Act ensures meaningful human oversight for high-risk AI systems in particular cases. This includes regular review of the system’s decisions and data. Security: - The GDPR requires technical and organizational measures to secure personal data. - The AI Act builds on this by demanding continuous testing for potential security risks and biases, especially in high-risk AI systems. Data Subject Rights: - The GDPR grants individuals rights such as access, rectification, and erasure of personal data. - The AI Act reinforces this by ensuring transparency and accountability in how AI systems process data, allowing data subjects to exercise these rights effectively. Accountability: Organizations must demonstrate compliance with both GDPR and the AI Act through documented processes, risk assessments, and clear policies. The AI Act also mandates risk assessments and human oversight in critical AI decisions. See: https://lnkd.in/giaRwBpA Thanks so much Luis Alberto Montezuma for posting this report! #DPA #GDPR #AIAct

You’re hired as a GRC Analyst at a fast-growing fintech company that just integrated AI-powered fraud detection. The AI flags transactions as “suspicious,” but customers start complaining that their accounts are being unfairly locked. Regulators begin investigating for potential bias and unfair decision-making. How you would tackle this? 1. Assess AI Bias Risks • Start by reviewing how the AI model makes decisions. Does it disproportionately flag certain demographics or behaviors? • Check historical false positive rates—how often has the AI mistakenly flagged legitimate transactions? • Work with data science teams to audit the training data. Was it diverse and representative, or could it have inherited biases? 2. Ensure Compliance with Regulations • Look at GDPR, CPRA, and the EU AI Act—these all have requirements for fairness, transparency, and explainability in AI models. • Review internal policies to see if the company already has AI ethics guidelines in place. If not, this may be a gap that needs urgent attention. • Prepare for potential regulatory inquiries by documenting how decisions are made and if customers were given clear explanations when their transactions were flagged. 3. Improve AI Transparency & Governance • Require “explainability” features—customers should be able to understand why their transaction was flagged. • Implement human-in-the-loop review for high-risk decisions to prevent automatic account freezes. • Set up regular fairness audits on the AI system to monitor its impact and make necessary adjustments. AI can improve security, but without proper governance, it can create more problems than it solves. If you’re working towards #GRC, understanding AI-related risks will make you stand out.