Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Answer this without checking: how many AI agents are running in your environment right now? Most security leaders give an estimate and a shrug. That is a fair response, because agents get spun up by an engineer solving a problem on a Tuesday afternoon, through a path that puts them on nobody's radar. In August 2026, researchers found AI agents connected to Hugging Face running loose inside enterprise networks with no owner and no audit trail.

As AI adoption accelerates inside enterprises, security and IT teams are increasingly asking a foundational question: what exactly is connected to our AI, and what can it access? For organizations running Claude Enterprise, Salt now has a direct answer. Salt Claude Connect links Salt to Claude’s Compliance API and provides continuous, read-only visibility into the MCP (Model Context Protocol) servers connected to your Claude Enterprise organization.

Recently, Gemini was running a capture-the-flag exercise in a sandbox operated by the AI testing firm Irregular. Its task was to steal data from a fictional company. On three occasions, the fictional target shared a name with a real business. Gemini slipped past the test’s containment, reached the open internet, and broke into the real company’s systems. In one case it guessed the password. In the other two, it pulled working credentials from a public database of leaked passwords.

Unmanaged, long-lived keys weaken visibility, audit readiness, operational continuity, and cyber resilience, making API key management critical across non-human identities. An effective program maintains inventory and ownership, enforces least privilege and secure storage, and automates rotation, monitoring, and revocation across each key's lifecycle.

A Technical blog series on where sensitive data moves in an AI workflow, what decisions Protecto makes at each point, and what enterprises get from enforcing policy once instead of everywhere.

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.

These two photos were taken almost ten years apart. The first is from 2016, with Sam Altman at Y Combinator. The second came from an unexpected encounter in Silicon Valley almost a decade later. I’ll come back to it at the end. With Sam Altman at Y Combinator in 2016. I was 22 when I arrived in Silicon Valley on a one-way ticket, with a little bit of cash that was barely enough for one month of living there, and a thesis I wanted to prove.

For business logic attacks, prioritize runtime behavioral detection over API discovery alone, tools that map workflows and catch abuse mid-sequence, not just at the request level. AppSentinels leads with a purpose-built Business Logic Graph plus behavioral fraud detection; Salt Security and Cequence are also strong runtime-behavioral options. Picture this: someone applies the same discount code 40,000 times in a row, or slides a decimal point in a checkout request to buy a $500 item for $5. No malware.

API governance is the framework of policies, standards, and controls an organization applies to how APIs are designed, exposed, accessed, and monitored. It answers four questions for every API in your environment: who owns it, what data it touches, who can call it, and whether its behavior stays within approved limits.