Microsoft Expands Project Perception to Rein In AI Agents






Microsoft expanded Project Perception on 19 October to track and contain internal AI agents, treating them as potential security risks.

Think of an AI agent as a digital assistant that can open files, use software and make decisions with little supervision.

Microsoft says the access these assistants need also opens a door to trouble. The company announced the update on Monday.

It adds tools to Microsoft 365 E7, including Agent 365, and builds on Project Perception, a security system first shown in July and in public preview since 3 August.

Hayete Gallot, executive vice president of Microsoft Security, says an agent does not have to be hacked to cause problems.

“Agents are unpredictable,” she wrote. They can change course as situations shift, and they can team up with other agents to reach their goals.

So Microsoft wants constant supervision. The headline addition is Guardian Agents, AI watchers that monitor other agents around the clock.

When behaviour turns risky, they can cut an agent’s access or block an action. Permissions work like a temporary visitor pass.

A new agent starts with no authority and gets only what its task needs, for a limited time. Bigger actions can require a person to approve them.

Nadim Abdo, a corporate vice president at Microsoft, gave an example. An expense-approval agent reads an outside document carrying a planted instruction.

Its risk score rises, so the system lets it keep reading but stops it approving or changing anything until a person signs off.

Microsoft is also hunting for agents already running on company devices. It added 35 more types, bringing the total to 60, including Claude Code, OpenAI Codex and GitHub Copilot.

The update follows a 10 October essay by CEO Satya Nadella, who argued that powerful AI models should be treated like potential insider risks, with safety controls that work independently of the models.

Microsoft says watching agents is only part of the answer. Gallot said future tools must also explain why a model makes its choices.

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