Microsoft urges organizations to secure AI Agents as insider threats, using dynamic permissions, continuous risk monitoring and automated containment.Microsoft published a detailed blog post explaining how organizations should secure AI agents. Microsoft sees them as a growing insider threat, not just a possible risk in the future. AI agents are not dangerous by nature, but they can access critical systems and may fail, be manipulated, or be compromised.“The most trustworthy agentic system will not be the one with the model we trust most. It will be the one designed that enables us to trust the model least.” said Satya NadellaThis is not just a theory. It is a principle that should guide how organizations design and secure AI systems.The post makes a distinction worth holding onto. An agent that starts a task with the right permissions and behaves correctly can still become dangerous if its context changes mid-run. A prompt injection in an external document, a piece of untrusted data that shifts the agent’s reasoning, a task that evolves in a direction nobody anticipated, none of these require the agent to be compromised in the traditional sense. The model produces variable outputs, the context changes, and the agent’s behavior drifts. At machine speed, the window between that drift starting and it causing damage is very short.Microsoft’s proposed answer is continuous containment rather than upfront trust. You define what the agent is allowed to do before it acts, you monitor what it actually does while it runs, and you adjust its permissions in real time as its behavior or the risk calculation changes. An expense approval agent should start with access scoped exactly to reviewing and approving expenses, nothing broader, and those permissions should shrink or disappear the moment the agent tries something outside that task.The post introduces Dynamic Permissions to apply the principle of least privilege to AI agents. An agent should not automatically get the same access rights as the employee who deployed it. It starts with no permissions and receives only the access needed for its approved task. These permissions remain active only while the task is running. “The employee retains the employee’s access.” Microsoft states. “The agent receives its own identity, permissions and its own authority scoped to its current task and for the duration of the task only.”Alongside that, Dynamic Agent Risk is a continuously updated score that combines signals from identity, endpoints, network, cloud, runtime, and the agent’s own behavior to assess whether what the agent is currently doing still matches what it was authorized to do. The response when risk rises is graduated rather than binary: preserve reads while blocking writes, narrow accessible resources, require human approval for high-impact steps, or stop execution entirely if the risk warrants it.“Dynamic Agent Risk combines signals from identity, data, endpoints, cloud, network, runtime, and the agent’s own activity. It considers how securely the agent is configured, what resources it can reach, the potential impact if something goes wrong, and whether its actions are drifting from the user’s or organization’s intent.” Microsoft states.“The result is not a one-time score assigned at onboarding. It is a continuously updated understanding of the agent’s current risk and whether its current authority still matches that risk. “Organizations frequently have AI agents running locally or introduced through third-party tools that were never formally registered in any central system. Microsoft says it’s expanding discovery to 60 types of local agents, including Claude Code, OpenAI Codex, and GitHub Copilot. Getting those into a unified registry with accountable identities is the prerequisite for applying any of the governance controls described in the rest of the post.You can’t set least-privilege permissions for an agent you don’t know exists. You can’t detect behavioral drift from an agent that doesn’t have a tracked identity. The agent registration problem is logistically harder than it sounds, especially for organizations where individual employees can spin up agents without IT involvement, which is most organizations right now.Microsoft recognizes that organizations cannot manually review every risk alert when they run hundreds of thousands of AI agents. Its solution is Guardian Agents, AI agents focused on security. They follow playbooks created by human operators, investigate changes in risk, apply or recommend security measures, and use what they learn to improve security policies. Humans set the goals and approve important decisions, while the agents handle continuous monitoring and response.However, this approach raises another concern. Guardian Agents are also AI agents, so they can fail or be manipulated, just like the agents they are supposed to control. Microsoft says security controls must operate outside the AI model and must not be changed by it. But this raises an important question: how can organizations ensure these controls remain reliable when the system enforcing them is also an AI agent? This is a challenge that real-world deployments will need to address in the coming years.“Organizations are adopting agents to serve customers better, solve problems faster, and give people more time for the work that matters most. The goal is not to make every model perfectly predictable before it can be useful.” Microsoft concludes. “The goal is to build systems that remain secure even when the model is not.”Follow me on Twitter: @securityaffairs and Facebook and MastodonPierluigi Paganini(SecurityAffairs – hacking, AI Agents)