This post was originally published on Finextra (Security)
What are the top security risks with AI agents, and how can they be avoided?
Why is identity the foundation of secure AI adoption?
How can AI agents be given autonomy, but ensure that users are kept in control?
In what ways can retrieval-augmented generation (RAG) enhance security and scalability?
How can financial AI applications be given the identity layer they require?
As financial services adopt AI agents to power customer service, risk analysis, fraud detection, and more, the security stakes couldn’t be higher. With attackers using the same tools, the old security playbook no longer works.
Identity plays a critical role in securing AI systems, especially AI agents that act on behalf of users. By looking at the key risks, including impersonation and over-permissioned agents, the financial services industry must learn how to protect sensitive data and workflows without slowing innovation.
Further, customer identity and access management (CIAM) can help to control which agents can access which APIs, data, and actions, while keeping users in the loop when it matters most. Techniques like Retrieval-Augmented Generation (RAG) can improve both agentic AI performance and security, for instance.
Today, identity solutions can help financial institutions adopt AI securely,
— Read the rest of this post, which was originally published on Finextra (Security).
