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Not without limits. An AI agent should not receive blanket authority over security decisions: if it is compromised or manipulated, it may use legitimate access to systems and data to carry out actions its operators did not intend. Whether a particular agent can be trusted depends on its permissions, integrations, identity, oversight and testing—not on the label “AI agent” or the model alone.
What did Adarsh Kant Sinha warn about?
In an interview published on August 27, 2026, The Cyber Express spoke with Adarsh Kant Sinha, identified by the publication as founder and CEO of ANVE.AI, about agent access to workplace systems, governance, prompt injection, red-teaming and the distinction between copilots and agents. Sinha’s central concern, as presented in the feature, is that an agent connected to business systems could turn legitimate permissions into a route for unauthorized action if it is compromised or manipulated. The feature describes risk scenarios; it does not report incident statistics or results from tests of a named agent. Read the interview feature.
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The article’s framing is useful, but it is an interview feature rather than an empirical study or vendor-neutral security assessment. Its claims should be read as Sinha’s perspective, not as independently measured findings.
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A scripted workflow generally follows a predefined sequence. An agent, by contrast, may choose among tools or steps to pursue a goal. That difference matters: the more routes and actions available to a system, the more carefully its authority needs to be bounded. “AI agent” is not a precise security classification, so an organization should examine what a system can actually do rather than relying on its product category.
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The interview names possible connections to Slack, email, customer relationship management (CRM) systems, financial systems and cloud infrastructure. These integrations can be useful, but they also define the potential reach of a compromised or misled agent. The relevant question is not only whether the model responds correctly; it is what the full system can access and change.
What to establish before granting an agent authority
Use these questions to determine whether an agent’s authority fits its task. They synthesize the interview’s concerns with NIST’s risk-management and identity guidance; they are not a checklist quoted from Sinha.
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- Identity and credentials: Which identity does the agent use, where are its credentials held, and can actions be attributed to that agent rather than ambiguously to a person or shared account?
- Access and scope: Which data, applications and tools can it reach? Are permissions limited to the specific task, or do they grant broader access than the task requires?
- Independent action: What can it do without approval? Identify actions that could disclose data, alter access, move money or disrupt services, and consider their potential impact and reversibility.
- Human intervention: For consequential actions, what exactly does a reviewer see, how much time do they have, and can they block the action before it occurs? A human-in-the-loop label alone does not answer these questions.
- Auditability: Are requests, tool calls, approvals and outcomes logged in a way that lets the organization reconstruct what happened and who or what initiated it?
- Testing: How will the organization evaluate manipulated inputs, prompt injection, unsafe integrations and failure paths—not just normal task completion?
How NIST guidance can help frame the decision
NIST’s AI Risk Management Framework (AI RMF) offers a voluntary structure for identifying, assessing and managing AI risks. Its four functions—Govern, Map, Measure and Manage—help organizations treat risk as a lifecycle responsibility rather than a one-time model check. The framework calls for defining and documenting human-oversight processes. NIST states that the AI RMF is “intended for voluntary use”; it does not certify a particular agent as safe. NIST says AI RMF 1.0 is being revised. See NIST’s AI Risk Management Framework overview.
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How to make a trust decision in practice
- Define the task and its boundaries. State what outcome the agent is meant to achieve, which systems it needs and which actions fall outside its role.
- Limit permissions to that scope. Avoid giving an agent broad standing access when a narrower identity, tool set or action permission will work.
- Separate low-impact steps from consequential actions. Allow routine, reversible tasks to proceed only within explicit limits; require meaningful approval for actions with serious or hard-to-reverse effects.
- Make activity attributable and reviewable. Preserve logs of the agent’s identity, inputs, tool use, approvals and resulting changes so that operators can investigate and intervene.
- Evaluate realistic misuse and failure cases. Test how the complete system behaves when inputs are manipulative, an integration is unsafe, or an action fails—not just whether it completes its intended task.
- Reassess when the system changes. New tools, integrations, credentials or autonomous actions can change the risk profile, so authority should be reviewed as the system evolves.
The governing principle is proportionality: the more consequential, irreversible or difficult-to-audit an action is, the less appropriate it is to let an agent take it independently. Trust should attach to a specific configuration and scope of authority, not to the general idea of an agent.
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