Banks have long invested in identity and access management to protect critical systems, customer data, and financial transactions. But cloud adoption, automation, and AI are changing who and what requires access.
Applications, workloads, services and AI-enabled systems all need access to banking resources. As these environments become more distributed and automated, security leaders must maintain control without adding persistent credentials, fragmented policies, or governance gaps.
What security teams are facing today goes beyond managing a growing number of identities. They must determine what autonomous systems can access, which actions they can perform, and how those activities are monitored as operations move at machine speed.
How can financial institutions manage access across cloud, AI, and hybrid environments while supporting compliance, auditability, and operational resilience?
»Identity is becoming a governance challenge
Modern banking depends on automation. Applications interact with databases and cloud services. Infrastructure is provisioned dynamically. Fraud detection platforms analyze transactions in real time. AI-enabled systems may need access to transaction platforms, fraud detection services, customer data sources, and operational workflows, creating new access-control and governance considerations.
A recurring theme during the recent AI Governance for Banking: Securing Agentic AI at Scale roundtable was the need for clearer controls around AI decision-making. As AI agents gain access to more systems and data, the focus shifts to defining what they should be authorized to do. That requires policies, accountability, and oversight that support regulatory compliance and AI risk management.
Each workload, application, and service requires access to resources, and every request must be authenticated, authorized, and governed. As machine identities spread across public cloud, private cloud, and on-premises infrastructure, security teams need controls that can work consistently across environments.
The real concern is how credentials are created, distributed, and used at machine speed, often across systems that span multiple teams, tools, and environments.
»Where credential risk accumulates
Applications rely on secrets, tokens, certificates, and credentials to connect with databases, APIs, and infrastructure services. Over time, these credentials can become dispersed across tools and environments, creating long-lived access, fragmented controls, and visibility gaps.
Security teams need to know where credentials reside, what’s using them, and whether the access is still required. They must also provide evidence that access to critical systems is controlled, monitored, and auditable.
Agents may interact with multiple systems, APIs, and data sources to complete a task. Without strong identity controls, organizations can struggle to understand what authority has been delegated, how access is being used, and whether activity remains aligned to policy.
For organizations investing in banking automation, access governance now extends beyond employees and contractors. Machine identity, nonhuman identity security, secrets management, and credential management are becoming core requirements for securing AI agents, APIs, and automated workflows. As AI adoption grows in banking, reducing credential sprawl and strengthening API security are becoming important elements of a broader AI governance framework.
»Govern access across hybrid environments
For security leaders, machine identity management is both a security responsibility and governance requirement. Access policies must work across cloud and on-premises environments, even as workloads change and infrastructure scales.
A centralized, policy-based approach gives teams a consistent way to authenticate machine identities, issue time-limited credentials, apply least-privilege policies, and preserve an auditable record of access activity across environments.
Banks preparing for wider AI adoption will need these controls in place long before large-scale autonomous operations become commonplace.
Roundtable participants emphasized that AI initiatives require the same level of oversight, policy enforcement, and accountability already expected of other critical banking systems. That visibility must extend from identity assignment and model interactions through API access and execution. Those capabilities support AI compliance, cloud governance, and operational resilience while improving visibility into autonomous activity.
»The case for identity-based security
Identity-based security changes how access is granted. Instead of using a static credential stored in an application or configuration file, a workload authenticates with a verified identity. Policy determines what it can access, and a credential can be issued for a specific purpose and a limited period.
This model can help financial institutions:
Reduce credential and access risk
Apply least-privilege access more consistently
Limit reliance on persistent credentials
Maintain a clearer, auditable record of access activity
Improve machine identity and service account management
Protect the credentials behind open banking and partner APIs
Support regulatory compliance and operational resilience objectives
Consistent controls can reduce credential-management overhead and simplify governance of sensitive access across cloud, on-premises, and hybrid environments.
Solutions like Vault enable this model by authenticating machine identities and issuing dynamic, policy-based credentials rather than relying on static secrets.
»Prepare for agentic AI
Agentic AI brings added urgency to an issue banks already face. As autonomous systems take on more operational tasks, access decisions increasingly occur without direct human involvement. Governing that activity requires organizations to verify identity, limit authority, enforce policy, and maintain auditable records of access at runtime.
During the AI Governance for Banking discussion, speakers noted that successful AI adoption depends on more than model performance. Organizations need operational discipline, visibility across workflows, enforceable controls, and clear ownership of outcomes. These capabilities are critical as banks address AI risk management objectives, Digital Operational Resilience Act (DORA) compliance initiatives, and hybrid cloud security requirements.
Vault, together with the IBM® Agent Identity capability, gives banks the foundation to run AI agents safely across hybrid cloud environments. Agent Identity registers each agent and ties its delegated authority to a human user. Vault turns that delegated authority into identity-based access, issuing dynamic, short-lived secrets scoped to least privilege and capturing every request in a complete audit trail. Every action traces back to an accountable person, and access can be revoked at the source the moment something looks wrong.
The solution supports the outcomes security leaders are accountable for: stronger governance, improved auditability, reduced operational burden, and greater confidence in access controls as banking infrastructure becomes more automated.
»Learn more about Vault
Banking infrastructure is becoming increasingly automated, prompting many institutions to reassess how machine identities, secrets, and access policies are governed.
Many organizations are also evaluating how AI governance, AI compliance, cloud governance, and resilience requirements fit within wider security and risk-management strategies.
Explore how financial institutions are approaching risk remediation, open banking API security, nonhuman identity governance, and secure AI adoption with financial services risk remediation.
