The CISA guide on agentic AI security highlights a critical debate for security engineers: should autonomous AI systems be treated as privileged users needing identity controls, or as application components requiring software security controls? Your decision shapes your entire security architecture.
Agentic AI systems—capable of making decisions, taking actions, and interacting with other systems independently—are already operational in production environments. They're involved in approving transactions, modifying infrastructure, and accessing sensitive data. According to CISA, these systems introduce unique risks, such as expanded attack surfaces and privilege creep, which don't align neatly with existing frameworks.
Treating AI Agents as Privileged Users
Many security teams default to identity and access management (IAM) controls because they are familiar. An AI agent that can modify databases or approve transactions resembles a service account with elevated privileges.
This approach aligns with existing controls:
- Create dedicated service accounts for each agent.
- Apply least-privilege principles through role-based access control (RBAC).
- Monitor authentication attempts and privilege usage through your SIEM.
- Enforce MFA where possible and rotate credentials regularly.
For teams working toward SOC 2 compliance, this fits into CC6.1 (logical access controls) and CC6.2 (authentication).
The practical advantage: your current IAM tools are applicable. Your audit team understands service account management, and your runbooks cover credential rotation and access reviews.
Treating agents as users also addresses privilege creep. You can apply time-based access controls, require approval workflows for privilege escalation, and implement session monitoring. Regular access reviews catch agents with unnecessary permissions.
Treating AI Agents as Application Components
The alternative view is that AI agents are not users making authenticated requests—they're code executing logic. Treating them as identity principals overlooks the actual attack surface.
Traditional IAM controls don't address core risks. An AI agent isn't compromised through phishing but through prompt injection, training data poisoning, or adversarial inputs. MFA and access reviews won't catch an agent manipulated into exfiltrating data within its authorized scope.
This perspective leads to application security controls:
- Implement input validation on all prompts and external data the agent consumes.
- Use output encoding to prevent unintended command execution.
- Apply secure development lifecycle (SDLC) practices—threat modeling, security testing, code review.
For AI-specific risks:
- Validate and sanitize all training data.
- Implement guardrails that constrain the agent's actions.
- Log every action the agent takes with full context.
- Test against adversarial inputs during development.
This approach aligns with NIST Cybersecurity Framework functions, especially Identify (ID.RA) and Protect (PR.DS). It also maps to ISO 27001 Annex A controls around secure development.
Where Practitioners Actually Land
Most security teams implement both control sets because risks span both categories. You need IAM controls to limit agent access and application security controls to prevent manipulation within that scope.
The real question: which control set do you prioritize when resources are limited?
Teams in regulated industries—financial services, healthcare—often emphasize IAM controls first. They need clear audit trails showing access and monitoring. Their compliance frameworks require these controls.
Teams with engineering-first cultures prioritize application security controls. They see the agent as part of their application stack, not as an external user.
The integration challenge is real. Existing frameworks weren't designed for components making autonomous decisions based on probabilistic reasoning. PCI DSS v4.0.1 Requirement 6.4.3 covers scripts and custom code but doesn't consider code that generates its own logic based on external inputs.
Our Take
Treat AI agents as privileged users for access control and monitoring, but as application components for security testing and validation. This dual approach acknowledges that these systems span traditional boundaries.
Start with IAM controls for immediate risk reduction:
- Create dedicated service accounts with least-privilege access.
- Implement comprehensive logging of all agent actions.
- Set up alerts for unusual behavior patterns.
- Conduct regular access reviews.
Then layer in application security controls specific to AI risks:
- Build input validation for all data the agent consumes.
- Implement output constraints to prevent actions outside defined boundaries.
- Test against adversarial scenarios during development and after model updates.
- Maintain separate environments for development, testing, and production.
The expanded attack surface CISA warns about comes from both vectors. An attacker might compromise the agent's credentials (IAM risk) or manipulate its decision-making through crafted inputs (application risk). Your controls need to address both.
For compliance teams: map AI agent controls to both identity management and secure development requirements. Don't force-fit agents into a single control category. When auditors inquire about AI system security, provide answers covering authentication, authorization, input validation, and behavior monitoring.
The tradeoff: this dual approach requires coordination between your IAM and application security teams. Establish shared responsibility models and joint review processes. This organizational overhead is real but less costly than discovering your AI agent was compromised due to incomplete controls.
AI Security Guidelines
