Identity in the Age of AI

In the world of financial services, AI is table stakes if you want to survive — it's the engine driving a new era of productivity, from automating complex internal workflows to powering hyper-personalized customer experiences. As banks and insurers race to harness the benefits, they're not just deploying a new kind of software; they're creating a new kind of digital persona, from the AI agent that processes a loan application to the chatbot that provides real-time financial advice.

The challenge is that this powerful new capability also dramatically expands the attack surface for fraud, raising the stakes for human and machine verification.

Imagine a customer applying for a personal loan on a bank's app late one night. Within minutes, an AI agent takes over, instantly validating their identity, cross-referencing documents, and performing a real-time credit risk assessment without any human intervention. This agent then sends a conditional offer directly to the customer's phone, automating a process that previously took days or even weeks.

Now imagine that the customer isn't exactly who they claim to be, or that a malicious agent has compromised the system, initiating a fraudulent transaction.

Sam Altman from OpenAI recently spoke about this as well, stating that there is an "impending, significant fraud crisis." Altman noted that society is unprepared for the rapid evolution of AI technology and called for an overhaul of how consumers access personal accounts.

This is the dual nature of AI in finance: the incredible promise of automation and the looming threat of sophisticated fraud.

That's why building identity into your AI workflows isn't just a security consideration — it's the foundational layer required to unleash the full potential of AI in finance.

AI Breaks Traditional Security

The problem is that traditional security models fail in an AI-driven world. Every business is facing two new mandates.

First: You have to bring AI into your products. We're not just talking about a feature; we're talking about new user experiences powered by customer-facing agents, internal copilots, and intelligent workflows.

Second: You have to make your products AI-ready. This means they need to be ready for other agents and systems to interact with them securely.

This represents a significant shift in how financial institutions design and architect their systems. You can't just bolt identity onto these agents after the fact. You must build them securely by design so they integrate seamlessly into your identity security fabric.

But this isn't as simple as it sounds. Securing agents with identity is fundamentally different from securing a traditional application.

Building Identity into AI Workflows with Okta

To ensure that these agents are being developed with a secure identity, financial services leaders should ask their teams these key questions:

Who is this agent? Just like a human employee, every AI agent needs a unique, auditable identity. Leaders should ask: "How are we authenticating and authorizing our AI agents?". A financial institution might have an AI agent that automatically processes loan applications. This agent's identity must be verified before it can access sensitive customer data or initiate a credit check.

What is the agent's least privileged access? The principle of least privilege is critical. Leaders should ask: "Does this agent have the minimum level of access it needs to do its job, and nothing more?". For an agent handling customer support inquiries, this means they should only have access to information relevant to that customer's account, not every customer's data or every internal system.

How do we ensure the agent is acting on behalf of the right person? Agents often act as a user's proxy, so it's vital to ensure the agent is authorized to retrieve and use data the user can see. This brings us to a crucial question: "How do we verify the human-in-the-loop for high-risk actions?". For example, if a conversational AI agent detects a potentially large-sum transaction, it should prompt the human user for real-time, out-of-band verification, such as a push notification or a biometric check, before proceeding.

Is every action auditable? For compliance and trust, every action taken by an AI agent must be logged and auditable. Leaders should ask: "Can we track every decision and action made by our AI agents to create a clear audit trail?". This means that for tasks such as automated compliance checks or fraud alerts, the audit trail should clearly show what the agent did, why it was done, and when.

The same AI that promises unprecedented efficiency can also introduce catastrophic risk. That’s why to secure identity in the age of AI, you need something that’s: easy to integrate with your stack, flexible enough to power the needs of your business, and purpose-built to deliver security and great user experience.

The future of finance is autonomous. But that autonomy is only as secure as the identity controls that underpin it. The time to build that future is now.

“To win in the AI era, you must get identity right.”