By Adam Mills

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The systems designed to protect AI are struggling to keep pace with how quickly it is evolving. That gap is no longer theoretical.

Last year, researchers at Anthropic demonstrated an AI system autonomously carrying out most of the steps involved in a cyberattack. The finding was not notable because it revealed a new vulnerability, but because it showed how quickly AI systems can now operate without human intervention.

What once unfolded over days or weeks can now happen in minutes.

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That acceleration is forcing a quiet rethink across cybersecurity teams. As AI systems become more autonomous—connecting tools, accessing data and acting on their own—the old model of periodic reviews and reactive defense is starting to break down.

In 2026, security leaders say, protecting AI looks less like auditing software and more like managing a living system.

From Human-Speed Threats to Machine-Speed Risk

For decades, cybersecurity assumed human pacing. Attacks took time to plan, credentials were stolen gradually and defenders could rely on logs, alerts and after-the-fact reviews to catch problems before they escalated.

AI breaks that assumption.

“The big shift is speed and autonomy,” said Mohammed Aboul-Magd, vice president of product for SandboxAQ’s Cybersecurity Group. “As intrusions become highly automated, the window between a minor oversight and a catastrophic breach collapses.”

When AI agents can probe connections, reuse credentials or link systems together on their own, the margin for error shrinks dramatically. A single misconfiguration—one forgotten access key or overly broad permission—can cascade across systems before anyone notices.

The challenge is compounded by the fact that most security tools were built for static infrastructure and human behavior. They assume predictable workflows, clear login boundaries and periodic review cycles. Autonomous systems don’t behave that way.

AI agents don’t log in like employees. They don’t “clock out.” And they don’t fit neatly into identity models designed around people.

As a result, risk accumulates quietly, often out of sight.

“Security has to move from occasional checks to continuous posture management for AI,” Aboul-Magd said. “Know where AI is used, what it can access, and whether it is leaking data or credentials.”

The Quiet Explosion of Machine Identities

One of the least visible, and fastest-growing, sources of risk is machine identity.

Every AI agent relies on credentials: API keys, certificates, tokens and service accounts that allow software to access data, systems and networks. As AI becomes more agentic, the number of these non-human identities multiplies rapidly.

“We expect an ‘agentic’ split in identity security,” Aboul-Magd said. “One track for people, and one for machines.”

Unlike human credentials, machine identities often lack clear ownership. They may be created automatically, shared across systems or left in place long after the job they were meant to perform has changed. Over time, they become forgotten access paths—still valid, still powerful and rarely monitored.

Aboul-Magd said those identities need to be treated less like permanent access passes and more like temporary badges.

“Non-human identities like cryptographic keys and accounts used by software rather than people will be managed as fast-changing, temporary badges that refresh themselves often and expire soon so old badges can’t be used,” he said.

In 2026, security leaders expect this shift to become unavoidable. Without short-lived credentials and clear ownership, small configuration issues can quietly become major exposures.

When Shadow AI Becomes Shadow Operations

The problem is intensified by how AI is actually adopted inside companies.

Employees are no longer just experimenting with standalone tools. Increasingly, they are building their own AI agents, wiring systems together with application programming interface (API) keys and granting access on the fly, often without IT or security teams fully aware.

“Unapproved or unmanaged AI creates blind spots,” Aboul-Magd said. “In 2026, we expect ‘shadow AI’ to morph into ‘shadow operations’ as employees create their own AI agents and grant too much access through API keys without the IT team’s oversight.”

At that point, the risk extends beyond data leakage. An unsupervised agent with broad access can disrupt critical systems or trigger unauthorized actions, turning what once looked like a productivity shortcut into an operational liability.

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What a Mature AI Security Program Looks Like

If this year is the inflection point, what does preparedness actually look like?

According to Aboul-Magd, mature AI security programs treat AI systems the same way organizations treat other foundational infrastructure—not as experiments, but as assets that require constant governance.

“A mature 2026 program treats AI like any critical system,” he said.

That means keeping a live inventory of every model and agent, regularly checking what they can access and how they handle data, enforcing simple rules around permissions and credential lifetimes and monitoring continuously for unusual behavior.

“Treat it as a loop,” he said. “Inventory, risk analysis, policy, monitoring, repeat.”

As AI systems spread, that visibility becomes critical not just for security teams, but for regulators, auditors and boards, groups increasingly responsible for overseeing how autonomous systems operate inside organizations.

Preparing for What Comes Next

As AI becomes embedded deeper into operations, boardroom conversations are shifting.

The question is no longer whether a company is using AI. It’s whether leadership understands where it runs, what it touches and who is accountable when something goes wrong.

Among the questions Aboul-Magd says executive teams should be asking:

• Where is AI used across our environment, and who owns each model or agent?
• What data can these systems access, and how do we prevent prompt injection and data leakage?
• Which policies govern AI, and how do we monitor and respond to incidents?
• Given the growth of machine identities, are we rotating keys and credentials automatically, or managing them manually?

Those questions reflect a broader shift already underway: AI security is becoming an operating discipline, not a technical afterthought.

The lesson from Anthropic’s findings is not that AI attacks are inevitable. It’s that the pace of change has accelerated beyond what periodic oversight can handle.

“We expect a move from one-off checks to continuous posture management: discovering where AI is used, assessing risk, enforcing policy, and monitoring pipelines,” said Aboul-Magd.

Updated 1/9/26 at 3:00 p.m. ET: This article was updated for clarity.

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