Explanations

    Agentic AI Security Explained

    Definitive reference pages on agentic AI security: what each term means, the risks it creates, the architecture involved, the controls that work, and the sources behind every claim.

    What Is Agentic AI Security? Definition, Risks, Architecture & Controls

    Agentic AI security protects autonomous AI agents, the tools they call and the AI Principals that create and command them. Definition, risks, architecture, controls and FAQ.

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    What Is AI Agent Security? Definition, Threats, Architecture & Controls

    AI agent security protects individual AI agents — their identity, tools, memory and actions — from compromise and misuse. Definition, threats, architecture, controls and FAQ.

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    What Is Shadow AI? Definition, Risks, Examples & Controls

    Shadow AI is any AI tool, model or agent used inside an organization without security, privacy or governance approval. Definition, risks, examples, controls and FAQ.

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    What Is Injected AI? Vendor-Injected Agents, Risks & Controls

    Injected AI is agentic AI pushed into your environment by a trusted SaaS vendor — enabled by default, granted your existing permissions, and never reviewed. Risks, examples and controls.

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    What Is an AI Principal? Definition, Types, Security Risks & Controls

    An AI Principal is code with creation authority: it spawns AI agents, trains their models and issues their commands. Definition, types, attack scenarios, controls and FAQ.

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    What Is an Agentic SOC? Definition, Architecture, Benefits & Controls

    An agentic SOC uses autonomous AI agents to triage, investigate and respond to security alerts alongside human analysts. Definition, architecture, benefits, risks and FAQ.

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    AI Agent Security vs LLM Security: What's the Difference?

    LLM security protects model inputs and outputs. AI agent security protects actions, identities, tools and memory. A side-by-side comparison with risks, controls and FAQ.

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    AI Agent Security Platforms: Capabilities, Categories & Evaluation Criteria

    What an AI agent security platform must do: discovery, identity, tool control, runtime tracing, containment and creation-layer coverage. Categories, criteria and buyer FAQ.

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    Agentic AI Security Risks: Top Threats, Examples & Mitigations

    The main agentic AI security risks — prompt injection, excessive agency, shadow agents, memory poisoning, tool abuse, multi-agent cascades and Principal compromise — with mitigations.

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    OWASP Agentic AI Security: Threats, Mitigations & How to Apply Them

    How OWASP's GenAI Security Project frames agentic AI threats and mitigations, how they relate to the OWASP LLM Top 10, and how to operationalize them in an enterprise.

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    MCP Security: Securing Model Context Protocol Servers & Tools

    Model Context Protocol connects AI agents to tools and data. MCP security covers server trust, tool-definition integrity, credential scope, consent and monitoring. Risks and controls.

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    AI Agent Governance: Framework, Policies, Controls & Metrics

    AI agent governance defines who may create agents, what they may access, how they are reviewed and how they are retired. Framework, policies, controls, metrics and FAQ.

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