I think one of the most underestimated security problems right now is token sprawl, given blindly to some autonomous python code driven by a poor man’s LLM..

Not because it is new. It is not. And not because nobody understands it. Most security teams do know sprawl beckons trouble. The problem is that it still gets treated like background hygiene when it is really an access control problem hiding in plain sight.

Token sprawl is the uncontrolled growth of API keys, service accounts, access tokens, and other machine credentials across cloud platforms, CI/CD pipelines, SaaS tools, internal apps, and now AI systems. It is what happens when machines start getting identities faster than humans can govern them.

That was already a problem before AI. Now it is becoming a scale problem.

Autonomous AI assistants and agents like openclaw creates demand for more connectors, more agents, more integrations, more temporary projects, more experimentation, and more machine-to-machine access. Every one of those things tends to generate credentials. And unlike human access, machine credentials often live in the shadows. They are easy to create, easy to forget, and hard to clean up once they spread.

That is where the real risk starts.

Some of the survey data around this lines up with what many teams are already feeling operationally. More than 16% of organizations say they do not track the creation of new AI-related identities. Nearly a quarter take more than 24 hours to rotate or revoke a credential after possible exposure. Around 30% take over a day to triage a high-severity credential leak. And almost a third spend more than 24 hours each month manually managing and reviewing non-human identities.

To me, that is the story. Not just that there are too many tokens, but that the systems around them are too slow, too manual, and too incomplete.

The most dangerous part of token sprawl is persistence. A pilot project ends. A tool gets disabled. An agent is retired. But the credential often stays behind. That means access can outlive purpose, and those leftovers become quiet entry points across environments. Attackers do not need dramatic front doors when forgotten side entrances stay unlocked.

How AI makes the sprawl worse

First, it increases identity creation speed. Teams building AI features move quickly, often with pressure to prove value fast. That usually means spinning up services, plugging tools into each other, granting access, and keeping the project moving. Governance tends to lag behind the build.

Third, legacy IAM processes are not built for this pace. A lot of organizations still rely on approval chains, spreadsheets, manual reviews, and fragmented ownership models for non-human identities.

That may have been survivable when machine credentials were growing at a slower rate. It is not survivable when AI projects start multiplying them across the environment.

This is why I think “we can rotate within 24 hours” is a false comfort. In a modern attack path, 24 hours is not fast. It is generous. And that assumes the organization even identifies the exposure quickly enough to start the clock. If triage itself takes a day, the actual window is even wider.

That is why token sprawl is not really a “more keys” problem. It is a persistent blast-radius problem.

If you have incomplete inventory, slow revocation, and heavy manual overhead, then credentials begin to outlive their original context and accumulate risk quietly over time. Nobody notices until something is abused, and by then the question is not just what leaked, but what else that credential could reach.

So what actually helps?

First, track credentials at creation or accept that you do not really control them. Every token, service account, or machine identity tied to an AI workload should have origin data, ownership, purpose, and expiration attached to it. Unknown-origin credentials should be treated as findings, not curiosities.

Second, shorten credential lifetimes by default. Static API keys are still everywhere because they are easy, but easy is usually just another word for future problem. Short-lived credentials with automated renewal dramatically reduce how long forgotten access can linger.

Third, automate revocation for suspected exposure wherever you can. If the only way to kill a leaked credential is to gather three teams on a call and hope someone remembers the process, then the process is broken. Revocation should be operationally real, not something that exists only in an incident response deck.

Fourth, tie every machine identity to a real owner. Not a vague platform group. Not a shared mailbox. A real team with responsibility for renewal, review, and removal. One of the biggest reasons token sprawl persists is that nobody feels safe deleting what nobody clearly owns.

Fifth, measure this like risk reduction, not maintenance. Time to detect, time to triage, time to rotate, time to revoke, percentage of credentials with owners, percentage with expirations, percentage created through approved workflows. Those numbers tell you whether the problem is shrinking or simply getting hidden better.

I would add one more thing that often gets skipped: design exit paths before deployment. Every AI project should answer a basic question up front: when this experiment ends, who cleans up the credentials, integrations, and service accounts it created? If there is no answer, then the project is already creating future sprawl.

That is the bigger issue here. Token sprawl is not just sloppy secrets management. It is governance debt made operational. AI is accelerating delivery, but it is also accelerating the creation of unmanaged machine access. If organizations keep treating that as routine maintenance, they are going to keep expanding their attack surface faster than they reduce it.

And that is exactly how small credential problems turn into large security incidents.