AI adoption is moving faster than most security programs were built to handle. Teams are already connecting large language models and AI agents to sensitive data, and the rules for protecting those systems are still taking shape.
No single checklist covers every AI law. Laws and frameworks such as California SB 53, the EU AI Act, and NIST AI RMF apply to different organizations and use different language. But across the ten we reviewed, two security requirements appear repeatedly: protect AI models from unauthorized access and exfiltration, and protect training data and model logic from poisoning. Both depend on identity, access, and data controls that security teams already manage.
This eBook connects those requirements to the security controls behind them and shows how Netwrix identity and data security products support each one, so you have answers when regulators, auditors, and customers ask how you manage AI risk.
In this eBook, you will explore
- How AI laws and frameworks turn principles like transparency and accountability into operational security controls.
- Why model exfiltration and model poisoning keep showing up as the core technical requirements.
- Three ways AI amplifies existing risk: permission sprawl, high-value privileged identities, and sensitive data moving through new channels.
- The six security controls that support AI regulatory readiness, from data discovery to incident investigation.
- How Netwrix Auditor, Access Analyzer, Data Classification, Privilege Secure, Endpoint Protector, and Change Tracker map to each control.
- A five-step roadmap and seven questions security leaders should be able to answer.
Why download this eBook
- Build your program around six controls that hold up as laws change.
- Find where excessive access and standing privilege expose AI models and training data.
- Test your readiness with seven questions before a regulator or auditor asks.
- Give security, GRC, and AI platform teams a shared set of controls to work from.
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