Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Forward Predict: Know the Impact of Your Network Changes Before You Push

What if your team could know exactly what a network change would do before it touched production, not a best guess, not built on incomplete data, but a mathematically verified outcome drawn from an accurate model of your actual network? That is what Forward Predict delivers.

What Is MCP Security? 9 Things Every CISO Needs to Know

Your AI agents had a productive day. Nobody can tell you what data they touched. A developer opens Cursor and connects it to a GitHub MCP server and a Postgres MCP server. The agent reads the repo to understand a schema change, finds an AWS access key in a config file, and uses it to run a migration against staging. The key now lives in the agent's context, in the Postgres query log, in the chat history, and in whatever artifact the developer copies out. No alert fired. No policy triggered.

Using Generative AI for Incident Response Automation: A Complete Guide to AI Agent Development

Security Operations Centers run on caffeine and context-switching. Any given shift means hundreds of alerts, tools that don't talk to each other, and analysts who know that somewhere in that noise is a real threat - they just need time to find it. That's the core tension AI agent development is built to resolve. This guide covers the full lifecycle: from scoping your first use case to maintaining a production-grade agentic SOC.

What Finance Teams Actually Want From AI

Of all industries, it feels like it's the finance industry that's in the best position to benefit from AI integration, especially finance teams. After all, it's those teams that typically have to manually deal with data - and that's just the kind of thing that AI can help with. With that said, though AI can be beneficial for finance teams, it's far from a slam dunk. AI integration among finance teams has been slower and less extensive than it could have been, and that's in large part because employees haven't been given the AI tools that they actually want, or which make their jobs easier.

What Is an Al Agent in Cybersecurity?

At the Milken Conference in May 2026, Robert F. Smith, founder and CEO of Vista Equity Partners, described a shift that every security leader should hear. Software, he said, has moved through three states: product, then service and now worker. "That agent, that software, actually does work." Companies that do not make the transition to software as a worker, he was blunt, risk being disintermediated entirely.

Security infrastructure for building AI in SecOps

Some of the security industry is still cautiously evaluating its relationship with AI. They are weighing questions, sitting with uncertainty, and waiting for something to ease their concerns about trusting AI in production. This post isn't for that group. This is for AI tool developers already in motion. The ones who vibe-coded a log parser over a weekend, spun up local inference on dedicated hardware, or ran cross-model research pipelines across multiple data sources.

AI builders can now easily access 1Password secrets management and developer tools

AI coding tools have changed who builds software. The barrier to entry has dropped to the point where a designer, an analyst, or a first-time founder can turn an idea into a working app in an afternoon. That shift is real, and it's accelerating.

How to Reduce Alert Fatigue in AI Agent Detection: Why It's a Unit-of-Detection Problem, Not a Triage Problem

When AI agent workloads start generating more alerts than your SOC can keep up with, the instinct most teams reach for is to deploy more triage on top of what they already have. If the SIEM is producing thousands of atomized alerts, plug in something downstream that can cluster, prioritize, and auto-resolve them faster than a human can. The market has consolidated around exactly this answer.

What Your Board Gets Wrong About AI Security

Editor's note: This article was originally published by Craig Riddell on LinkedIn. It has been republished here with the author's permission. Boards are giving AI security more airtime than ever. What they're not giving is the right framing. A year or two ago, AI was mostly a question of experimentation risk. Today, it's tied directly to revenue, customer experience, operational efficiency, and competitive advantage. The urgency is real, and it's translating into aggressive deployment timelines.

Prompt Analysis for AI Attack Detection: Four Signal Categories, Three Blind Spots, One Correlation Layer

At 2:47 PM on a Tuesday, a customer support agent receives a routine ticket asking about return policy edge cases. The agent retrieves a section from your internal policy wiki through RAG to formulate the response. Three weeks earlier, an attacker had planted a hidden instruction in that wiki page. Bedrock Guardrails scored the retrieved context at 0.04 — well within benign range.