Across financial services operations, machine identities play critical roles, but in many organizations, these cryptographic keys, API tokens, certificates, and service accounts remain chronically under-governed. What’s more, machine identities outnumber human identities by staggering margins, creating a massive, often unseen, unsecured attack surface—one that’s only further compounded by the rise of artificial intelligence (AI).
The good news? Organizations can solve machine identity governance. But doing so requires treating machine identities as integral, “first-class citizens” in cybersecurity planning. As AI transforms the threat landscape, financial organizations must rethink how they govern access and manage risk. Treating machine identities as VIPs is now essential to safeguarding operations and staying resilient.
The privileged access paradox in banking: Speed vs. control
One of the most acute challenges for financial services is what some call the “privileged access paradox.” Institutions must balance two opposing forces:
- The need for robust privileged access controls to meet regulatory requirements and mitigate risks.
- The pressure to enable seamless speed and agility in fast-moving environments like trading desks or DevOps operations.
This paradox often results in risky compromises. Developers may hold sweeping privileges to systems, traders might access sensitive infrastructure without sufficient controls, and compliance officers require deep access for investigations. These are legitimate use cases, but each becomes a potential vulnerability without strong governance.
Machine identities complicate matters even further. Unlike humans, they aren’t confined to set working hours or behaviors. They exist to automate tasks, often operating across siloes and touching critical systems without much, if any, direct oversight. A single compromise—like a misconfigured API or expired certificate—can lead to cascading failures or open the door to malicious actors.
Financial organizations cannot afford to lean into ad-hoc solutions that solve short-term convenience while exposing long-term security gaps.
Why machine identity governance is the missing link in financial cybersecurity
The explosion of machine identities in financial services is an undeniable reality. Unfortunately, most organizations lack clarity on managing their lifecycle, prioritizing risks, and governing their use. Machine identities aren’t inherently difficult to secure, but neglecting to integrate them into existing frameworks creates blind spots in even the most sophisticated cybersecurity strategies.
Key challenges include:
- Discovery gaps: Service accounts and API keys are often created for temporary projects and then forgotten. These abandoned identities create hidden vulnerabilities.
- Lifecycle inconsistencies: Machines rarely have defined identity lifecycles. Certificates expire, credentials go unmanaged, and ownership is ambiguous.
- Scalability issues: Reliance on manual processes for managing machine identities doesn’t scale in a financial system handling millions of automated requests.
For many institutions, focusing on human-centric identity and access management (IAM) tools has left machine identities operating in the shadows. And in the age of AI, the stakes continue to rise.
How financial institutions should govern machine identities: 5 essential steps
To tackle these challenges, financial organizations need to shift identity governance practices from human-driven frameworks to interconnected systems that value all identities—human, AI, and machine—for their role in operations.
Here’s how they can begin this transformation.
1. Discover and inventory all machine identities
It’s impossible to govern what you can’t see. The first—and arguably most critical—step is to thoroughly inventory all machine identities, including the entire spectrum of identities, from service accounts to API tokens.
Strategies for success:
- Deploy automated discovery tools that continuously scan for new identities across systems and networks.
- Map each machine identity to its function, owner, and level of access. This creates accountability and prevents dormant identities from being forgotten.
2. Establish end-to-end machine identity lifecycle management (creation → rotation → decommission)
Every machine identity has a lifecycle, from creation to decommissioning. Without clear governance at each stage, outdated and unused identities accumulate, creating weak links in your security posture.
Key lifecycle actions to implement:
- Regularly rotate credentials to reduce exposure.
- Set expiration dates for both certificates and access tokens, ensuring they align with operational needs.
- Define explicit ownership for every identity, complete with timelines for review or renewal.
3. Classify machine identities by risks posed to critical systems
Not all machine identities carry the same risk profile. For instance, an API key touching customer payment systems presents far greater stakes than one used for internal batch processing. Effective governance means prioritizing based on impact.
Risk-based approaches include:
- Classify machine identities by access scope and associated systems.
- Assign risk levels based on operational criticality, data sensitivity, and exposure to external threats.
- Use automation to spot unusual behaviors—such as a dormant identity suddenly becoming active.
4. Use AI for anomaly detection, automated remediation, and intelligent privilege controls
Ironically, addressing the challenges introduced by AI also depends on harnessing AI’s capabilities. AI-driven tools can help manage the velocity and volume of machine identities with precision. In particular, intelligent privilege controls apply risk- and context-aware decisions at access time to help enforce the principle of least privilege (PoLP) without adding unnecessary friction.
AI security use cases:
- Continuously analyze access patterns for signs of misuse or compromise.
- Automate identity revocation if risky behavior is detected.
- Apply intelligent privilege controls to: just-in-time (JIT) access and zero standing privileges (ZSP); time-bound elevation; session isolation and monitoring; credential vaulting and rotation; and risk-based step-up authentication.
5. Add human oversight for compliance and risk governance
While automation is essential, some level of human oversight is vital to ensure that decisions align with business objectives, regulatory expectations, and ethical considerations. Financial organizations must find a balance between efficiency and human judgment.
Machine identity governance roadmap for financial services
To help financial institutions move forward, here’s a step-by-step roadmap to help teams build more resilient machine identity governance frameworks.
Phase 1: Discovery and dependency mapping of machine identities
- Conduct an enterprise-wide inventory of all machine identities and map interdependencies.
- Assess risks tied to identity sprawl and develop a detailed risk register.
Phase 2: Build foundational governance (ownership, rotation, renewal)
- Establish clear ownership structures for machine identities.
- Deploy automated lifecycle management tools to handle certificate renewals, API key revocations, and more.
Phase 3: Incorporate AI capabilities (anomaly detection, auto-revocation)
- Start with low-risk AI deployments, such as anomaly detection on non-sensitive systems.
- Gradually expand to AI-enabled credential rotations and adaptive privilege access.
Phase 4: Continuous improvement against emerging AI-driven threats
- Focus on iterative improvements to governance strategies.
- Monitor for emerging AI-driven threats and adjust frameworks accordingly.
Balancing AI innovation and security in financial services
AI and machine identities represent the future of financial services, offering unmatched speed and scalability in everything from algorithmic trading to real-time fraud detection. However, without strong governance frameworks like Basel III operational risk requirements and the evolving SEC guidance on AI, they risk destabilizing the very systems they’re meant to enhance.
In financial services, the stakes are uniquely high. A compromised machine identity could execute unauthorized trades worth millions, manipulate Know Your Customer (KYC) and Anti-Money Laundering (AML) processes, or expose customer financial data across multiple jurisdictions. Unlike other industries, financial institutions operate in 24/7 global markets where a security incident at 3 a.m. can cascade into systemic risk before human oversight can intervene.
Taking control of machine identity governance extends beyond technology—it’s a business imperative. Organizations can reduce vulnerabilities by becoming proactive stewards of identity lifecycle management, risk classification, and regulation compliance while staying agile in an increasingly competitive landscape.
The puzzle of governance isn’t about stopping technological innovation—it’s about securing it in a way that satisfies regulators from the SEC to the FCA, while maintaining competitive advantage. If you haven’t read my previous post—The AI revolution in financial cybersecurity—check it out for a deeper dive into why identity and AI risks have become so urgent for financial services. Tackling this challenge today can help position institutions to thrive tomorrow.
Andy Parsons is the director of EMEA Financial Services and Insurance at CyberArk.
🎧 Listen in: Want to hear more from Andy Parsons on the future of financial cybersecurity, machine identity governance, and AI’s evolving role in banking? Tune into his appearance on the Security Matters podcast. It’s a deep dive into the real-world challenges and opportunities shaping the industry.
