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Agentic AI Security: The Enterprise Playbook

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Overview

Explore AI security with the Lakera LLM Security Playbook. This guide is a valuable resource for everyone looking to understand the risks associated with AI technologies.

Ideal for professionals, security enthusiasts, or those curious about AI, the playbook offers insight into the challenges and solutions in AI security.

Highlights

  • Comprehensive Analysis of LLM Vulnerabilities: Detailed overview of critical security risks in LLM applications.
  • Gandalf - The AI Education Game: Introduction to Gandalf, an online game designed for learning about AI security.
  • Expansive Attack Database: Insights from a database of nearly 30 million LLM attack data points, updated regularly.
  • Lakera Guard - Security Solution: Information about Lakera Guard, developed to counteract common AI threats.‍
  • Practical Security Advice: Tips on data sanitization, PII detection, and keeping up-to-date with AI security developments.

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Overview

Agentic AI Security: The Enterprise Playbook is a practical handbook for securing AI systems that no longer just generate content, but take action.

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As AI evolves from copilots to autonomous agents with memory, planning, and tool access, the security model must evolve with it. This playbook explains why traditional controls, built for structured inputs and static execution paths, cannot adequately govern systems that reason over language, assemble dynamic context, and execute actions across enterprise tools.

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Rather than focusing on model safety in isolation, the guide reframes AI risk as a system-level challenge. It maps where sensitive data actually flows inside AI-driven workflows, identifies the new execution surfaces introduced by agent autonomy, and outlines a unified architecture—the AI Defense Plane—for discovering, governing, and protecting AI usage across employees, applications, and agents.

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Built for security leaders and architects, this playbook moves beyond theory. It provides a clear architectural model, real-world production examples, and a framework for securing AI at enterprise scale without slowing innovation.

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Highlights

Key themes explored in the playbook include:

  • AI has crossed from output to action. The primary risk is no longer incorrect answers, but unintended behavior—data exposure, tool misuse, and unsafe autonomy.‍
  • Sensitive data flows through new paths. Prompts, retrieved context, model outputs, and agent tool calls create interaction-based disclosure channels traditional DLP and perimeter controls cannot see.‍
  • Three enterprise AI exposure surfaces. Risk now spans workforce AI usage, AI applications embedded in business logic, and autonomous agents operating at runtime.‍
  • MCPs create a new execution boundary. Model Context Protocols standardize how agents connect reasoning to action, concentrating risk at the moment intent becomes execution.‍
  • A unified AI Defense Plane. Securing AI at scale requires coordinated visibility and enforcement across employees, applications, and agents—not isolated point solutions.