What AI Agent Safety Can Learn from Traditional InfoSec

What AI Agent Safety Can Learn from Traditional InfoSec

AI agent safety feels like a new field. The terminology is new. The threat models are new. The tools are new. But the underlying principles are as old as computer security itself. At Authensor, we've deliberately built SafeClaw on the foundations of traditional information security, adapting decades of proven principles to this new context.

Here are the lessons we've borrowed.

Defense in Depth

No single security control is sufficient. Traditional InfoSec deploys multiple layers: firewalls, intrusion detection systems, access controls, encryption, logging, and incident response. Each layer catches what the others miss.

SafeClaw applies defense in depth to AI agents. The action classifier is the first layer. Workspace boundaries are the second. Rate limiting is the third. Risk signal detection is the fourth. Human escalation is the fifth. Audit logging underlies everything. Any one of these layers might be insufficient alone — together, they create a robust defense.

Principle of Least Privilege

Grant only the minimum permissions needed for the task at hand. This principle, formalized by Jerome Saltzer in 1974, is the foundation of access control in every operating system, database, and cloud platform.

SafeClaw applies least privilege to AI agents through policy profiles. A coding agent doesn't need network access, so the default policy denies it. A documentation agent doesn't need to execute shell commands, so those are denied too. Each agent gets only what it needs, and nothing more.

Fail-Closed Design

When a security system encounters an error or uncertainty, it should default to deny. Fail-open systems create security gaps when they malfunction — exactly when you need them most.

SafeClaw is fail-closed throughout. Unknown actions are denied. Unclassifiable actions are denied. Configuration errors cause denial, not permissiveness. If SafeClaw itself crashes, the agent's tool calls fail rather than proceeding unguarded.

Complete Mediation

Every access to every resource must be checked. No shortcuts, no cached permissions, no bypass mechanisms. The reference monitor concept, introduced by James Anderson in 1972, requires that security checks be comprehensive and inescapable.

SafeClaw mediates every agent action. There's no "fast path" that skips the classifier. There's no action type that's exempt from policy evaluation. The mediation is in the data path, not alongside it — actions cannot flow around it.

Audit and Accountability

Every action must be logged, and logs must be tamper-evident. Without audit trails, you can't detect breaches, investigate incidents, or prove compliance.

SafeClaw's session management produces comprehensive, append-only audit logs. Every action, every decision, every risk signal is recorded. The hash-chain design we use for audit logs makes tampering detectable.

Separation of Concerns

The entity that performs an action should not be the same entity that authorizes it. This is the principle behind code review, dual-key nuclear launch systems, and financial segregation of duties.

SafeClaw enforces separation of concerns between the AI agent and the authorization system. The agent proposes actions. SafeClaw evaluates them. The agent cannot modify SafeClaw's policies, bypass its checks, or influence its decisions. The authorization logic is outside the agent's control.

Usable Security

The most secure system in the world is useless if nobody uses it. Usability research in InfoSec has shown repeatedly that security mechanisms must be easy to use correctly and hard to use incorrectly, or they will be circumvented.

We've applied this lesson obsessively. The setup wizard gets users to a working configuration in 60 seconds. Sensible defaults cover most use cases. The dashboard makes security status visible at a glance. We'd rather SafeClaw be slightly less configurable and significantly more usable than the reverse.

What's Different About AI Agents

Not everything transfers. Traditional InfoSec deals with deterministic programs with predictable behavior. AI agents are stochastic — the same prompt can produce different actions. This means our monitoring and detection systems must handle behavioral variance in ways that traditional systems don't.

But the principles — defense in depth, least privilege, fail-closed, complete mediation, audit, separation, usability — these are universal. We're grateful for the decades of research that came before us.

Explore how we've applied these principles in SafeClaw's code on GitHub, and read about our architecture in the documentation.