Direct answers to the questions teams hit putting AI agents into production: what the terms mean, how the pieces differ, and where the risk actually sits.
By entry type
Agents gain capabilities through MCP servers, skills, plugins, extensions, packages and SaaS connectors. Each behaves differently at install, at execution and at review. Here's the taxonomy, and why allow-listing alone doesn't govern it.
How organizations attribute, predict, and control what autonomous agents spend — and why cost and security are the same instrumentation problem.
What actually turns a model into an agent — and why security controls have to target that layer, not the model itself.
Where hooks sit in an agent's execution, what they can and can't protect against, and how they compare across major platforms.
Agent skills carry a specific security property: parts of a skill can execute before the model reasons about whether to use it. Here's the mechanism, why refusal training and gateways both miss it, and what to check.
An AI agent audit trail is a durable record of what an agent did, under whose credentials, and in what context. Here's why agent logs are weaker evidence than most teams assume, what immutability actually means, and what auditors ask for.
What agent behavior means, how it differs from architecture and configuration, and why agent risk accumulates across sequences rather than appearing in single actions.
The EU AI Act, NIST AI RMF, ISO 42001 and the OWASP Agentic AI Top 10 applied to autonomous agents: what they have in common, why agents are harder to evidence, and why compliance is downstream of discovery.
The continuous practice of connecting what an agent is configured to do, what it can actually reach, and what it's actually doing — and how that differs from identity governance and point-in-time AI-SPM scans.
Exactly what EDR covers for AI agents, what it structurally can't, and where agent security sits alongside the controls already deployed.
What AIDR means, how it differs from AI-SPM and guardrails, and the question that separates real AIDR from monitoring with a new name.
Deterministic controls produce the same outcome every time for the same input. Probabilistic controls use a model to judge each case. Here's why the distinction decides what you can audit, certify and operate at scale.
An MCP gateway sits between agents and MCP servers, applying policy to the traffic that passes through it. Here's what that covers well, the tool types it structurally cannot see, and the architectural question worth asking before you rely on one.
MCP servers and agent skills solve different problems and are often confused as competing standards. Here's the actual architectural difference, and the security distinction most comparisons miss.
Non-human identity governs what an agent is permitted to access and under whose credentials. It doesn't establish what the agent did or who answers for it. Here's where the two differ and why both are needed.
Prompt injection is when content an AI agent reads is treated as instruction. Here's how direct, indirect and stored injection differ, why it can't be patched like SQL injection, and what actually reduces the risk.
What shadow AI is, how it differs from agent sprawl, why surveys and gateways miss it, and how detection actually works.
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