To manage security risks, treat AI agents like new employees. Provide them with their own isolated environment—separate accounts, scoped API keys, and dedicated hardware. This prevents accidental or malicious access to your personal or sensitive company data.

Frameworks from firms like KPMG and AWS emphasize that AI agents must be treated as entities with identities and permissions. A strong IAM foundation is a critical control layer to prevent agents from accessing or unintentionally leaking sensitive information, reflecting a broader shift to treat agents like any other privileged user in an IT ecosystem.

Each AI agent acting on a user's behalf creates a new "non-human identity" with its own keys and API access. This proliferation of autonomous agents dramatically increases the number of potential exploit points, a problem traditional security models weren't designed to handle.

Simply giving an agent a user account is dangerous. An agent creator is liable for its actions, and the agent has no right to privacy. This requires a new identity and access management (IAM) paradigm, distinct from human user accounts, to manage liability and oversight.

Current agent frameworks create massive security risks because they can't differentiate between a user and the agent acting on their behalf. This results in agents receiving broad, uncontrolled access to production credentials, creating a far more dangerous version of the 'secret sprawl' problem that plagued early cloud adoption.

An AI agent cannot simply use a human's credentials. It requires its own identity, permissions, and access controls for security and traceability. This means SaaS companies will likely charge for agent seats, creating a significant new revenue stream.

To address security concerns, powerful AI agents should be provisioned like new human employees. This means running them in a sandboxed environment on a separate machine, with their own dedicated accounts, API keys, and access tokens, rather than on a personal computer.

The rise of autonomous software agents like Cognition's "Devin" introduces a new, critical security layer: agent identity. Organizations must decide if agents have their own unique identities or inherit them from the deploying user. This is fundamental for creating auditable logs and securing their actions.

Unlike deterministic workflows, AI agents can behave in unpredictable ways. The key to securing them is not to restrict every possible action, but to tightly control their identity and permissions. Knowing *who* the agent is and *what systems* it can access becomes the primary security control.