Before data is processed by AI systems, it can be anonymised or pseudonymised. This involves stripping or masking identifiers that connect data to an individual, making it difficult to trace back to the person without additional information that is kept separate.

Data used by Agentic AI can be encrypted both in transit and at rest. Encryption ensures that data is transformed into a secure format that only authorised systems and users can decode, protecting against unauthorised access.

Implementing strict access controls ensures that only authorised personnel and systems have access to sensitive data. This can include role-based access controls (RBAC), where permissions are granted based on the user’s role within an organisation.

Whether data is stored on local servers, on devices, or in the cloud, implementing secure storage practices is crucial. This involves using secure servers, employing strong authentication methods, and regularly updating security protocols to protect against vulnerabilities.

When aggregating data from many users to train AI models, differential privacy techniques can be employed. These techniques add noise to the data in such a way that the privacy of individual data points is maintained while still allowing for accurate aggregate analysis.

This is a technique where the AI model is trained across multiple decentralised devices or servers holding local data samples, without exchanging them. Thus, learning happens locally, and only the model improvements are shared and aggregated, not the data itself.

Regular audits help ensure that data handling practices comply with privacy laws and regulations such as GDPR, HIPAA, or CCPA. Compliance checks can help identify and rectify potential privacy issues before they become problematic.

This principle involves limiting the data collection to what is directly relevant and necessary to accomplish a specified purpose. By collecting only the data needed, Agentic AI reduces the risk of privacy breaches.

Providing clear and understandable data policies helps users know how their data is being used, what measures are in place to protect it, and how they can control their personal information. This transparency builds trust and ensures compliance with regulatory requirements.

Developing and following ethical AI frameworks that prioritise privacy is crucial. These frameworks guide the design, development, and deployment of AI systems with an inherent respect for user privacy.