AI security is an engineering problem. That means defined security requirements, enforceable controls, named owners and evidence that protections work. 

As AI becomes more capable, the industry must accelerate security engineering, broaden access to defensive tools and share what works faster. 

The internet and cloud computing changed how software operates, while core security responsibilities endured: establish identity, control access, limit exposure and verify that protections work.

AI agents introduce new capabilities — reasoning, using tools and adapting actions based on the data they encounter. Those capabilities require applying established principles to new operating conditions.

This pace creates pressure. Organizations want the productivity benefits of AI while the practices to govern and secure these systems are still developing. 

Applications depend on code, data, identities, services and infrastructure. Security depends on how those components work together — and AI agents extend that system.

Models provide capabilities; harnesses organize context, tools and workflows; and runtime environments provide the infrastructure within which actions execute. Each part of that stack carries security responsibilities, and proper protection requires controls across each layer, as data, instructions and actions move through the system. 

Consider an agent updating a customer record. Say it encounters malicious instructions in an attached document and attempts to export customer data to an unauthorized destination. 

A network policy should block the transfer, and protected logs should capture the attempted tool call, authorization decision and outcome so the security team can identify the tool used and the destination it attempted to reach. 

Permission to update a customer record should not automatically extend to exporting that data.

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