AI agents now handle myriad tasks in enterprises, from workflow automation and customer interactions to software orchestration and data retrieval.
Online development platforms such as Google Cloud's Gemini Enterprise Agent Platform and Microsoft Copilot Studio are accelerating the adoption of AI agents by making it easier for organizations to build and deploy AI systems at scale.
But as AI agents access enterprise applications, interact with APIs, trigger workflows, and make independent decisions, they introduce a new
identity-security challenge.
Traditional
identity and access management systems were designed primarily for humans and for service accounts, not autonomous systems that act dynamically and continuously.
Unlike narrowly restricted service accounts and other non-human identities, AI agents can exceed privileges, expose sensitive data, or interact with systems in ways that security teams cannot always foresee. There is a growing need for runtime identity security that governs what AI agents are allowed to do at the exact moment they act.
To address this challenge, Silverfort's identity-security controls are being
integrated into Google Cloud's Agent Gateway and Microsoft Copilot Studio.
These integrations bring real-time, identity-aware enforcement to AI agents, applying least-privilege access controls, contextual authorization, and continuous monitoring directly into agent workflows, recognizing the necessity of treating AI agents as first-class enterprise identities that require the same level of governance, accountability, and runtime protection as human users.
How Google, Microsoft and Silverfort centralize governance for agent communications
A new challenge has emerged: how to securely govern AI agents that interact with APIs, software tools,
cloud platforms, and data environments. Agent Gateway and Copilot Studio are designed to simplify the development and orchestration of AI agents, but organizations also need identity-aware controls that govern what those agents are permitted to do in real time.
Silverfort's integrations with both platforms aim to solve that problem by bringing centralized identity security into the runtime layer of agentic AI.
On Google Cloud, Silverfort integrates with Agent Gateway, the networking and control component of Google's Gemini Enterprise Agent Platform. The integration gives organizations visibility into how AI agents interact with APIs, enterprise systems, and external tools while enabling real-time identity validation and access enforcement.
A similar model applies with Microsoft Copilot Studio, where AI agents built with low-code and no-code tooling can authenticate, retrieve enterprise data, and execute workflows across cloud and on-premises environments. Every meaningful agent action ultimately ties back to a human user and multiple machine identities, creating complex chains of authentication and authorization.
By integrating directly into these platforms, Silverfort creates a centralized governance layer for human identities, service accounts, third-party agents, and enterprise AI systems alike. Organizations can manage agent-to-agent and agent-to-tool interactions through a unified control plane instead of relying on fragmented, platform-specific security policies.
Why securing AI agents must happen during runtime
Identity security was originally built around static authentication events such as user logins or API key validation. AI agents change this model because their behavior evolves during execution. An AI agent may begin with an approved task, but then try to access more systems, escalate its own privileges, or interact with sensitive data in unexpected ways.
Because of this unpredictability, Silverfort emphasizes runtime enforcement of AI agents instead of relying solely on preconfigured permissions or after-the-fact monitoring. In Copilot Studio, Silverfort evaluates every access request just as a Copilot agent begins to act, blocking unauthorized actions before execution.
This runtime model matters because AI agents operate quickly and autonomously. Static permissions can become overprivileged; retrospective logging may spot abuse only after the damage has been done. Runtime enforcement evaluates the context of an action continuously, including the identity behind the agent, the requested action, associated risk signals, and the current policy state.
Runtime protections can thwart unauthorized privilege elevation, stop unusual access attempts, adapt policies based on context, and generate full audit trails tied back to enterprise identity governance frameworks.
These development integrations matter because AI security cannot be bolted onto existing infrastructure. It must operate inline with every agent interaction, always validating whether requested actions are legitimate, expected, and authorized.
How AI agents are first-class enterprise identities
The rise of agentic AI forces organizations to rethink what constitutes an enterprise identity. AI agents are not passive automation tools. They reason, chain decisions together, interact with enterprise resources, and sometimes act independently of direct human supervision.
Silverfort's integrations with Google Agent Gateway and Microsoft Copilot Studio reflect a consensus that AI agents must be treated as first-class identities, much like employees, contractors, or privileged service accounts.
AI agents require the same core security principles applied to human users: least-privilege access, contextual authorization, continuous monitoring, and auditable accountability. Silverfort's platform connects every agent back to human "owners" while evaluating runtime activity against enterprise identity policies.
Because modern enterprises rarely operate just one AI instance or model, and deploy internal agents, third-party AI systems, and custom-built tools simultaneously, identity security must span many different AI ecosystems.
Silverfort's integrations with Google Cloud and Microsoft show how identity security is evolving into the enforcement layer for agentic AI. Rather than trusting agents implicitly once they authenticate, organizations must continuously validate what the agent is, what it is trying to do, whether it should be allowed to do it, and how that action aligns with organizational policy and risk posture.
