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An open-source, local, read-only MCP server that checks your request before Google quietly hands back nothing Ask the Google Search Console API for clicks filtered by country us and you get zero rows. No error, no warning, just nothing. It's not the only one.
TL;DR: The OWASP Top 10 for Agentic Applications is a list of the ten most critical security risks facing autonomous AI agents, published in December 2025. It is a taxonomy to assess exposure against, not a compliance mandate.
TL;DR: AI agent identity management replaces shared service accounts with agent-specific identities, scopes authorization to each task, and produces access records an auditor can follow from request to outcome.
TL;DR: AI agent orchestration is the coordination of multiple agents, tools, models, and shared state, and in production it only works when operational controls run inside every agent, tool call, and handoff. Each orchestration pattern fails in its own way, so each needs its own controls.
It is 2:14 in the morning. A payments reconciliation agent at a large bank starts pulling customer account records it has never touched before. Within seconds, a runtime control flags the behavior, the agent is quarantined, and a kill switch severs its access to every downstream tool.
TL;DR: The real test in the AI gateway vs AI control plane for SIEM logging question is what lands in your SIEM. A gateway's delivery telemetry tells a SOC that a request moved: which provider, whether it succeeded.
Most enterprise AI runs on models nobody in the building controls. A team calls OpenAI, Anthropic, or Google's API, and the actual inference happens on infrastructure the organization has a contract with, not infrastructure it operates. That arrangement is normal and often the right call.
Most conversations about protecting sensitive data in AI systems start with the prompt: what a user typed, what got pasted in, what a retrieval step pulled into context. That's the front door, and it matters. But a completely clean prompt doesn't guarantee a clean response.
MCP has become the default way AI agents connect to real systems: internal databases, business APIs, file systems, third-party services. Introduced in late 2024, it's since been adopted across OpenAI, Google DeepMind, Microsoft, and thousands of engineering teams, with SDK downloads now in the tens…
Plenty of platforms describe themselves as "private" or "on-premises" and still phone home. A license check, a telemetry ping, a background update call, a DNS lookup that reaches out before failing silently, any one of these is a live network path, and any outbound network capability at all means a…
Most conversations about prompt injection assume the attacker's instruction arrives through content: a message a user typed, a webpage an agent scraped, a document it summarized. Tool poisoning breaks that assumption.
Prompt injection sits at the top of OWASP's LLM Top 10, the most current ranking of risks facing production AI systems. Yet plenty of security teams still treat it like a chatbot novelty, something that makes a bot say something embarrassing rather than a real production security problem.
Gartner published its first Magic Quadrant for AI Governance Platforms in June and a Hype Cycle for AI Governance Technologies in August. Both are rigorous. Both answer the question they set out to answer. Our argument is with the title, "AI Governance," not the research under it.
Ask an enterprise AI governance team whether they have a program in place, and the answer is almost always yes. Ask them to prove, with numbers, that the program is actually working, and the conversation slows down.
If your compliance program only checks in on your AI systems once a quarter, you are not governing them for the other eighty-nine days.
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