How do Agentic AI agents learn?

Agentic AI learns from labelled examples, finds patterns independently, and improves decisions through rewards and feedback. Over time, it continually adapts its understanding based on new experiences and data, becoming more accurate and effective.

Agentic AI agents store learned information in their model parameters, external databases, or directly on devices. They may also use cloud storage for easy access, structured knowledge graphs for complex reasoning, or hybrid approaches combining multiple methods.
Protecting data privacy when using Agentic AI involves several strategies and technologies designed to safeguard sensitive information while still enabling the AI to learn and function effectively. Here are key measures commonly implemented:

1.

Data Anonymisation and Pseudonymisation

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.

2.

Encryption

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.

3.

Access Controls

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.

4.

Secure Data Storage

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.

5.

Differential Privacy

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.

6.

Federated Learning

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.

7.

Regular Audits and Compliance Checks

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.

8.

Data Minimisation

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.

9.

Transparent Data Policies

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.

10.

Ethical AI Frameworks

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.