Why do AI agents increase risk when they can act on behalf of users or services?

Why This Matters for Security Teams

Agentic systems change the risk model because decision-making and execution are no longer separated as cleanly as they are in traditional applications. Once an AI agent can call APIs, approve actions, create tickets, move data, or invoke code on behalf of a user or service, the security boundary shifts from “what did the model say?” to “what did the model cause to happen?” That is why guidance such as the OWASP Agentic AI Top 10 matters: it frames agent risk as a combination of prompt manipulation, tool abuse, and excessive authority.

The practical issue is delegation. If an agent inherits broad service credentials, it can turn a convincing instruction into a real-world side effect without a human noticing in time. NHI Management Group treats this as an identity and control problem as much as an AI problem, because the agent often acts through secrets, tokens, and service accounts that already exist in production. The strongest programs align AI governance with NIST AI Risk Management Framework principles so authority, accountability, and monitoring are designed in before deployment. In practice, many security teams discover this only after an agent has already exercised legitimate credentials in an unintended sequence, rather than through intentional testing.

How It Works in Practice

An AI agent increases risk when three conditions exist at the same time: it can interpret untrusted input, it can choose actions, and it has permission to execute those actions. That combination creates an attack path where prompt injection, malicious retrieval content, poisoned context, or deceptive user instructions can influence the agent’s decisions even if the underlying model is not “compromised” in the classic malware sense.

Operationally, the control question is not whether an agent is intelligent, but how tightly its authority is bounded. Best practice is to split the workflow into narrow steps: validate input, constrain available tools, require explicit approval for high-impact actions, and log every action with enough context for reconstruction. Where possible, the agent should operate with ephemeral or scoped credentials instead of long-lived secrets. Monitoring should also distinguish between model output and executed side effects, because the dangerous event is often the tool call, not the generated text.

• Limit the agent to the minimum tool set needed for the use case.
• Separate read-only actions from write, approve, or delete actions.
• Use step-up authorization for sensitive operations and human confirmation for irreversible ones.
• Track provenance for prompts, retrieved content, and tool outputs.
• Alert on unusual action sequences, not just unusual prompts.

Threat modelling should map the agent to known adversary patterns. The MITRE ATLAS adversarial AI threat matrix is useful for understanding manipulation of AI systems, while the CSA MAESTRO agentic AI threat modeling framework helps teams reason about agent workflow trust boundaries and escalation points. These controls tend to break down when an agent is wired into legacy automation with broad service-account access because the environment treats every execution as trusted until after damage occurs.

Common Variations and Edge Cases

Tighter control often reduces automation speed and increases operational overhead, requiring organisations to balance productivity gains against the cost of approvals, logging, and exception handling. That tradeoff becomes sharper in high-volume environments where agents are used for customer support, engineering workflows, or security operations, because friction can tempt teams to relax guardrails too early.

There is no universal standard for how much autonomy is acceptable yet. Current guidance suggests using risk tiers: low-risk informational tasks may tolerate broader autonomy, while financial, administrative, or production-changing actions need stronger checks. Edge cases appear when an agent chains together individually safe actions into an unsafe outcome, or when multiple agents share the same backend credentials and one compromised workflow expands into others. This is where identity governance becomes central: a service identity that can act across systems should be treated as a privileged non-human identity, not just a technical account.

Another common blind spot is memory and persistence. If an agent stores context, preferences, or instructions across sessions, an attacker may plant a delayed trigger that activates later under normal conditions. Teams should also be cautious with retrieval-augmented workflows, because untrusted documents can become instruction channels if content handling is not separated from control logic. NHI Management Group recommends treating agent autonomy as something to be intentionally earned, not assumed by default, and re-assessed whenever tool access, data scope, or business impact changes.

Related resources from NHI Mgmt Group

• What should teams do when AI agents act on behalf of real users?
• Why do AI coding agents increase code security risk if they are not verified?
• Why do AI agents increase risk when they are connected to HR systems with broad read access?
• Why do AI agents create additional risk when they can read and act inside Jira through MCP?