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Ai Governance

Articles about AI governance, responsible AI use, and organizational implementation of AI regulations.

50 articles

The agent register: what to record for each AI agent

No framework prescribes an agent register with fixed fields, but regulators and voluntary frameworks do recommend an overview of your agents. What do you record for each agent, how does the register differ from the AI register and the record of processing, and how do you keep it current?

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Security risks of AI agents: the lethal trifecta and the OWASP list

An AI agent can take a wrong action, not just give a wrong answer, and hidden instructions in an email or web page will probably never be fully preventable. This article explains the lethal trifecta, OWASP's list for agents and the real cases, and what the GDPR, the AI Act and the Dutch Cybersecurity Act ask of you.

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AI governance through the privacy function: from intake to DPO oversight

You do not need a separate AI bureaucracy. The record of processing activities, the DPIA, privacy by design, the DPO, data processing agreements and the breach process together form the chassis for the duties under the AI Act. This is how to run AI governance on the privacy function you already have: six building blocks, one register, a RACI, the limits of the DPO's role, the Dutch public sector as a worked example and a 90-day starting plan.

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Agentic AI under the EU AI Act: how to govern autonomous agents

AI agents that execute tasks autonomously fall squarely under the EU AI Act through the definition in Article 3(1). The complete guide: the risk ladder applied to agents, the roles in the value chain, GDPR Article 22 as the boundary that applies today, and a governance framework covering permissions, oversight, logging and a kill switch.

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AI Act, NIS2 and DORA: one control set, three regulatory views

The AI Act, NIS2 and DORA largely regulate the same underlying capabilities: risk management, incidents, third parties, governance and logging. Here is how to build a single control set where each law becomes a view, instead of running three parallel compliance programmes.

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Who is responsible when an AI agent makes mistakes?

An AI agent that acts autonomously can make mistakes with real consequences. Who carries the responsibility? The chain explained: model provider, platform provider and deployer, plus what boards and legal counsel should assign internally today.

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Algorithmic confidentiality privilege (ACP)

The algorithmic confidentiality privilege (ACP) is legal adviser Zahed Ashkara's proposal to give conversations with AI chatbots the same legal confidentiality as conversations with a doctor or lawyer. Published as an opinion piece in Het Financieele Dagblad, fully developed here.

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