Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

The problem is not a lack of controls. It is connecting them into one attack story. The more time I spend with enterprise AI deployments, the clearer one thing becomes: AI security is incredibly fragmented. There are LLM guardrails, AI gateways, MCP security tools, API security, endpoint controls, SASE, code scanning, and runtime detection. Each solves a real problem, but agentic systems do not experience them as separate layers, and neither do attackers.

On September 15, 2026, NIST published Internal Report 8587, “Protecting Tokens and Assertions from Forgery, Theft, and Misuse.” It’s built to extend NIST Special Publication 800-53 Release 5.1.1, and on paper it reads like an SSO hardening document for government agencies and their cloud vendors.

Every digital banking transaction today, whether it’s a fund transfer, a loan approval, or a balance check through a fintech app, runs through an API. API security for banking means protecting these connections from attacks that go far beyond simple hacking attempts: fraud rings probing for account access, bots automating fake transfers, and partners quietly overstepping the access they were given.

Here's the uncomfortable part first: in August 2026, researchers found AI agents connected to Hugging Face running loose inside enterprise networks. No owner, no audit trail, nobody who could tell you they existed until something broke. If that sentence made your stomach drop a little, good, because it should. It's the same blind spot most security teams are sitting on right now. They just haven't had their version of the incident yet.

Agentic AI has moved from experimental research to live production environments at unprecedented speed, outpacing nearly every technology security leaders have encountered in recent history. Distinguishing themselves from standard chatbots that merely respond and pause, autonomous agents architect multi-step workflows, interface with tools and APIs, maintain contextual memory, and execute operations with minimal human intervention.

Buying agentic AI security software is a fast-moving decision with high stakes. Get it wrong, and your security team ends up chasing agent activity it cannot see, while attackers exploit business logic gaps that no prompt filter was built to catch. This checklist gives security and platform leaders a structured way to evaluate vendors before signing, based on the questions that actually separate a purpose-built platform from a bolted-on feature.

APIs power modern software. They connect apps, move data, and run agentic AI workflows. But every API is also a door into your systems. Attackers know this. They target APIs more than any other layer today. This guide breaks down API security from the ground up. You will learn what it means, why it matters, and how to protect your APIs against real-world threats. We cover risks, the OWASP API Security Top 10, best practices, tools, and use cases. Let’s get started.

The volume of published incident research involving agentic AI is increasing, and the analysis that follows each report tends to concentrate on the same attribute: speed. The recent investigation from Unit 42, the threat intelligence and incident response group at Palo Alto Networks, is a representative case.