Anish Patel: agents operate faster and with more autonomy
Patel’s observation captures the central security problem federal agencies now face: AI is moving beyond prompt‑and‑response tools into agentic systems that can take independent action. An agent may be granted access to the same systems a human can use, but its ability to process data and act at scale creates the real risk. Patel framed the issue not as a changed attack surface but as a change in tempo — an agent can exploit an available surface “much faster than a human can.”
Vibe coding, API keys, and the hidden risks of AI-built applications
The rise of AI-assisted development — described in the source as “vibe coding” — is accelerating how employees build tools to solve immediate operational needs. That agility carries tradeoffs. Patel recounted encountering this at Cloudflare when employees began building AI‑powered applications that worked well but required API keys to function. Historically, those keys could be long‑lived and broadly scoped, and that breadth can grant access to far more data or intellectual property than a single application needs. The result: functionality that solves a mission problem can also open pathways to sensitive resources.
Patel emphasized that the risk is often not malicious insider intent but inadvertent exposure when a well‑meaning employee bypasses traditional development controls. He recommended designing credentials for narrowly defined tasks — for example, a “five‑minute agent cycle” — instead of relying on persistent credentials with broad access.
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End the scrambleLeast privilege must become dynamic and enforceable
Zero Trust and least‑privilege principles remain essential, but agencies must apply them in new ways. Patel described an operational model where agents start with restricted permissions and are granted additional access only as tasks demand it. That approach allows observers to evaluate how permissions are used and to gate expansion.
Importantly, Patel warned that prompts alone are insufficient as control mechanisms. “You can prompt an agent to say make sure you stay within this boundary, but that’s a suggestion. That’s not an actual protection mechanism,” he said. For agencies, the imperative is clear: enforceable, proactive controls must supplement guidance embedded in prompts.
Multiplayer access control: one agent, many users, different permissions
Another distinct requirement emerges when a single agent services multiple users. Patel labeled this “multiplayer access control.” A shared agent does not imply uniform authority: the agent must account for the identity and permissions of the human invoking it. In federal contexts — where employees may have widely varying roles and clearances — a shared agent cannot become a conduit that lets one user perform actions or retrieve information outside their authorization.
This dual layer of identity and access governance — the agent’s own permissions plus the permissions of the person making a request — must be explicit in policy and technical enforcement. As agencies broaden AI use, identity and access policies need to treat agents as first‑class principals subject to the same, and newly adapted, rules as human users.
What this means for federal agencies, security teams, and procurement leaders
- Federal agencies: Integrate agent identity and access controls into workflows that balance innovation and security, starting agents with limited permissions and evaluating requests to expand access.
- Security and engineering teams: Replace long‑lived, broadly scoped credentials with task‑scoped credentials (Patel’s example: a five‑minute agent cycle) and implement enforceable controls rather than relying solely on prompt constraints.
- Procurement leaders: Anticipate requests for new credentialing models and agent‑aware access management when assessing AI tools and services, including controls for shared, multiuser agents.
AI agents are changing both who builds government applications and how quickly those applications can interact with internal systems. The work ahead is technical and procedural: extend identity and access controls to autonomous systems, apply least privilege dynamically to tasks, and ensure shared agents respect the differing permissions of their human users. Without those changes, agencies risk scaling capability while simultaneously scaling exposure — a gap that, as Patel warned, is no longer theoretical but already manifest in operational environments.
