The Definition Problem

Ask ten CISOs what “AI security” means, and you’ll get ten different answers. The term lacks a clear definition - most organizations cannot articulate what it covers or how to manage it.

Often a significant part of what they mean is LLM or agentic security: managing how LLMs, tools, and agents access resources. This is a distinct challenge from traditional application security, requiring new patterns of control and visibility.

The Infrastructure Gap

Most organizations deploy AI agents without the security management infrastructure required to control them. This applies to both LLM and MCP/tool access. The gap manifests in several critical ways:

Attribution Failure

API keys are reused across applications, environments, and users. Even when provider dashboards show attribution, the data is buried in the provider’s interface, separate from your security operations. When an incident occurs, determining who or what initiated the action becomes an investigation instead of an immediate answer.

Policy Enforcement Breakdown

Security controls cannot be applied consistently when each service manages its own access. Testing policies before production is limited or nonexistent. Organizations end up hoping that security is implemented correctly rather than enforcing it systematically.

Centralized Management Gap

Credentials are distributed across repositories and config files. Provisioning happens in provider dashboards, disconnected from your infrastructure. The lack of programmatic key management means credentials cannot be managed securely at scale. Rotation becomes a manual process prone to errors and service disruptions.

Visibility and Detection Gaps

Interactions are logged across disparate systems - Sentry, APM tools, Prometheus, observability platforms. Security threats cannot be detected when visibility is fragmented. Your SIEM sees network traffic, your observability platform sees application metrics, but neither understands the semantic content of LLM interactions.

What AI Security Management Actually Requires

Moving from distributed chaos to secure-by-default deployment requires three integrated layers:

1. Provisioning

Centralized credential management with identity binding and programmatic control. Every AI service connection should be provisioned through a single system that maintains attribution and enables automated rotation. When credentials are managed centrally, security policies can be enforced consistently.

2. Policy Enforcement

Input/output security controls applied consistently at the connection level. Rather than trusting each application to implement security correctly, enforce controls at the infrastructure layer. Policies should be testable, versionable, and applied uniformly across all AI interactions.

3. Detection and Audit

Unified monitoring with real-time threat detection and attribution. Security operations need visibility into the semantic content of LLM interactions, not just network-level metadata. Threats should be detected in real-time, with clear attribution to specific users, applications, and workflows.

Why Current Approaches Fail

Without centralized provisioning, security controls become impossible to enforce consistently. The current approach - distributing credentials and hoping teams implement security correctly - does not scale.

Organizations trying to retrofit security onto distributed credential management face fundamental limitations:

  • No single point to enforce policy
  • No way to test changes before production
  • No unified view for threat detection
  • No programmatic control for automated response

The gap between current practice and what secure AI deployment requires is not a minor implementation detail - it’s a fundamental architectural mismatch.

The Path Forward

Defining AI security starts with acknowledging what doesn’t work: distributed credentials, fragmented visibility, and hoping teams implement security correctly.

Secure-by-default AI deployment requires treating credential management, policy enforcement, and threat detection as an integrated system. Organizations that build this infrastructure can move quickly while maintaining control. Those that don’t will continue managing AI security as an afterthought, discovering gaps only after incidents occur.

The question isn’t whether to build this infrastructure - it’s whether to build it proactively or reactively.