Why AI Security Requires a New Playbook

Security teams have spent decades perfecting defenses for deterministic systems. Firewalls, access controls, input validation: every tool in the kit assumes that software behaves the same way given the same inputs.

AI breaks that assumption. A large language model can produce different outputs from identical prompts. A fine-tuned model can carry biases from its training data into production decisions. An agent can take actions that no developer explicitly programmed.

This means traditional security frameworks leave blind spots. Prompt injection is not a variant of SQL injection. Model poisoning is not a variant of supply chain attacks. These are fundamentally new threat categories that demand new detection and response strategies.

A modern AI security posture starts with understanding the attack surface: model inputs, training pipelines, agent permissions, and output channels. It requires continuous monitoring, not periodic audits. And it demands a team that thinks about intent, context, and behavior, not just signatures and rules.