LLM Gateway Comparison 2026: Security and Governance
Portkey, LiteLLM, Kong AI Gateway, and Cloudflare AI Gateway compared for routing, spend control, and security — how to shortlist for your team.
AI agent security, identity, governance, cost, and the engineering behind the control plane.
Portkey, LiteLLM, Kong AI Gateway, and Cloudflare AI Gateway compared for routing, spend control, and security — how to shortlist for your team.
Agent credentials live in more places than human ones and last longer. How agent sessions get hijacked, how tokens get replayed, and the binding that stops it.
Govern AI customer-support agents: PII detection, response guardrails, escalation triggers, and tamper-evident audit logs regulators expect.
Evaluating alternatives to Noma Security for agentic AI security — red-teaming, agent containment, and where platform-bundled options fit.
Static budgets catch the disaster and miss the drift. Which agent spend signals are worth alerting on, why agents are easy to baseline, and how to avoid noise.
An agent estate is a graph of agents, tools, data sources, and models. What a topology map should show, what it answers that a list cannot, how to keep it true.
AI coding agents read files, run tools, and push code autonomously. Learn the real risks — prompt injection, secret leakage — and how to contain them.
A browser-use agent reads the open web and holds your session cookies: the highest-risk agent deployment shape in common use. Here is how to constrain it.
An operating model for enterprise AI agent governance: who owns identity, roles, delegated admin, central policy and audit as agents spread across teams.
Standing production access for agent operators is a permanent risk for an occasional need. How to build a break-glass path fast enough to use and safe to trust.
Denial of wallet attacks run up the bill until you take your service down yourself. How the attack works against agentic systems and which controls stop it.
A pragmatic guide to AI agent security for startups: the controls that matter most when you are moving fast and have limited security resources.
AI agents are deputies acting for principals. Confuse which principal an agent serves and its permissions become the attacker's. How to structure authority.
Crescendo and other multi-turn jailbreaks spread the payload so no single message looks malicious. Why stateless filtering misses them and what catches them.
How SaaS teams embedding AI agents keep multi-tenant data isolated, costs attributed per customer, and agent behavior governed at scale across tenants.
Excessive agency turns a model mistake into an incident. Its three components (excessive functionality, permissions, and autonomy) and the controls for each.
Red teaming AI agents is not prompt-hacking a chatbot: the objective is unauthorized action. A scope, a technique set, and a scoring model for useful findings.
What HIPAA and the EU AI Act ask of AI agents that touch PHI: minimum-necessary access, audit controls, risk classification and the evidence to keep.
A control plane in the request path is a dependency for every agent call. Which parts must fail closed, which can degrade, and how to plan each failure mode.
A RAG pipeline pulls attacker-influenced content into a model's context. How vector stores get poisoned, how retrieval leaks across tenants, and what holds.
Article 50 transparency obligations apply from 2 Aug 2026 — AI disclosure, synthetic-content marking, and what engineering teams must ship now.
What regulators expect of AI agents in banking and finance: EU AI Act high-risk uses such as credit scoring, GDPR duties, attributable actions and evidence.
Reconstructing what an AI agent did, why, and what it touched needs evidence most deployments never record. The minimum record set and how to investigate.
A claimed agent identity is worthless without verification. How decentralized identifiers and verifiable credentials establish it across trust boundaries.