The AI revolution in financial cybersecurity

Financial cybersecurity has never been a static discipline. Over two decades in this industry, I’ve seen it transform from a compliance checkbox to a cornerstone of business resilience—usually after a painful lesson. Today, we’re heading into the most significant paradigm shift for financial security since online banking: the convergence of artificial intelligence and machine identity governance.

AI in financial services isn’t new; we’ve been using machine learning and smart tools to improve decision-making and risk analytics since the early 2000s. But today’s AI—especially when paired with the explosion of machine identities in our digital ecosystems—demands that we rethink how we approach cybersecurity. The intersection of AI and machine identities is where the next decade of financial security will be decided. Most institutions, from what I see, are nowhere near prepared for what’s coming.

The rise of machine identities in financial systems

When I first stepped into CISO and CIO roles, identity management was a mostly human concern. We worried about traders with access to the wrong systems, engineers with excessive access to system-level configurations, compliance officers with too many privileges, and keeping roles separated to avoid conflicts of interest and toxic combinations. Machine-to-machine communications? They existed, but they were simple and manageable.

Now, that world is gone. In a typical tier-one bank, machine identities outnumber human ones by 96:1—sometimes even higher in the most automated trading environments. These aren’t just background database connections or forgotten API keys. They are the authentication backbone holding our entire ecosystem together: high-frequency trading bots, real-time fraud engines, compliance logging, and payment network interfaces. Machine-to-machine is the lifeblood of modern financial operations. And now, with AI agents autonomously making decisions, executing trades, and managing portfolios, each agent spawns its own constellation of machine identities.

Here’s a typical scenario: a single customer transaction might involve an app authenticating to an API gateway, the gateway reaching into the core banking system, multiple databases getting pinged for fraud checks, compliance systems tracking the event, and external settlement networks doing their thing. Every handoff involves a machine identity with its own credentials, privileges, and, too often, hidden vulnerabilities.

We spend billions protecting human access, but machine identities still run on service accounts created three years ago by engineers who’ve since left the company. These accounts are usually governed by manual processes that don’t scale. The result is a sprawling attack surface that I know from experience most security teams can’t even inventory, let alone secure properly.

AI in finance: balancing opportunity and risk

AI drops into this already complex mix and immediately rewrites the rules. The opportunity side is exciting: AI can analyze privileged access patterns at a scale no human team can approach, dynamically adjust controls in response to risk, automatically rotate credentials, and predict threats before they strike. It’s the security analyst you wish you could clone a hundred times over.

But I’d be lying if I said the risk side of the equation wasn’t just as real—and much more urgent. AI systems need deep, broad access to do their jobs. Take a financial crime compliance AI: it will need to see just about every transaction, behavioral profile, and threat feed. If that system is compromised, it’s not only the crown jewels at stake; it also provides a roadmap for exploiting the very system that’s been compromised.

Worse, we’re starting to see AI weaponized on the offensive side. Advanced persistent threat (APT) groups are already tinkering with AI tools that can map org structures, identify valuable machine identities, and orchestrate credential theft at a scale we’ve never faced. The same detection tools we use on defense can and will be turned against us.

Why machine identity governance is critical for financial cybersecurity

Most orgs miss the mark here because they’re using frameworks built for the last war—focused almost entirely on human users, with machine identities as an afterthought. That’s just not tenable anymore. The privileged access paradox is real: We’re under pressure to tighten machine identity controls without slowing business down, but old identity and access management (IAM) tactics create friction and breed workarounds.

With AI accelerating the pace, a reactive, manual approach falls apart. The path forward starts with giving machine identities first-class status in every security design.

Steps to strengthen financial cybersecurity with AI and machine identity governance

So what does progress actually look like?

You can’t secure what you can’t see. I’m talking about automated discovery beyond basic network scanning tools that can trace when your machine learning (ML) training pipeline spins up five new service accounts at 3 a.m., or when that fraud detection model starts authenticating to a new data lake you didn’t know existed. Most security teams are flying blind because their discovery happened once, during implementation, and never again. But continuous discovery could just be the key to sleeping better at night.

Every machine identity needs an owner, a process, and an expiration date. But here’s the new wrinkle: AI agents are creating their own identities on the fly. Your governance framework needs to handle a trading algorithm that spawns temporary identities for market data feeds, then kills them an hour later. Those outdated certificates and forgotten API keys aren’t just technical debt—they’re the unlocked backdoors attackers walk through. I’ve seen breaches that traced back to service accounts created for a pilot project that ran eighteen months ago.

Real-time monitoring becomes critical because machine identities don’t behave like humans. Some hibernate for months—like that disaster recovery bot that only wakes up during quarterly disaster recovery tests, or trading algorithms that activate only during specific market conditions. When they suddenly spike in activity, you need systems that can distinguish between legitimate business needs and compromise. Traditional behavioral analytics miss this entirely.

And here’s where it gets tricky: when you use AI to govern machine identities, you must govern the AI itself. Human oversight, regulatory compliance, and audit trails leave nothing to chance. Especially when your AI agents are creating and managing other AI agents, the recursive complexity can spiral beyond human comprehension quickly, and regulators are well prepared to ask pointed questions about algorithmic accountability in economically critical financial systems.

The companies getting this right aren’t treating it as a security problem; they’re treating it as an operational resilience problem that happens to have security implications.

The future of financial services security in the age of AI

Up next: I’ll share my perspective on turning machine identity governance challenges into practical solutions. In my next blog, “Solving the machine identity governance puzzle,” I’ll break down the steps financial institutions can take to help secure their future in an AI-driven world. Stay tuned!

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.