• Facilitating auditability by keeping a comprehensive record of data provenance, procurement, preprocessing, lineage, storage, and security.

security and Safety

AI systems should be safe and sufficiently secure against malicious attacks.

security refers to ensuring the cybersecurity of AI systems, which includes mechanisms against malicious attacks specific to AI such as data poisoning, model inversion, the tampering of datasets, byzantine attacks in federated learning5, as well as other attacks designed to reverse engineer personal data used to train the AI.

Security refers to ensuring the cybersecurity of AI systems, which includes mechanisms against malicious attacks specific to AI such as data poisoning, model inversion, the tampering of datasets, byzantine attacks in federated learning5, as well as other attacks designed to reverse engineer personal data used to train the AI.

Deployers of AI systems should work with developers to put in place technical security measures like robust authentication mechanisms and encryption.

Just like any other software, deployers should also implement safeguards to protect AI systems against cyberattacks, data security attacks, and other digital security risks.

Just like any other software, deployers should also implement safeguards to protect AI systems against cyberattacks, data security attacks, and other digital security risks.

It is also important for deployers to make a minimum list of security testing (e.g.

vulnerability assessment and penetration testing) and other applicable security testing tools.

Privacy enhancing technologies include, but are not limited to, differential privacy, where small changes are made to raw data to securely de identify inputs without having a significant impact on the results of the AI system, and zero knowledge proofs (ZKP), where ZKP hide the underlying data and answer simple questions about whether something is true or false without revealing additional information