Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

The Rise of the AI Security Engineer: A New Discipline for an AI-Native World

We are witnessing the birth of a new profession in the blend of security engineering and security operations, a discipline that didn't exist five years ago because the systems it protects didn't exist five years ago. As artificial intelligence moves from experimental to essential and agentic systems begin to perceive, reason, act, and learn autonomously, we need defenders who can operate at the same velocity. I'm talking about the AI Security Engineer.

Cloud Security for Financial Services: Building a Compliant AWS Environment

Financial services organizations moving to AWS often discover that retrofitting security and compliance controls costs three to five times more than building them in from the start. Compliance gaps discovered during audits can delay critical initiatives, trigger regulatory scrutiny, and expose organizations to unnecessary risk.

Why Your Security Stack Is Blocking AI (And How to Fix It)

Sr. Technical Content Strategist Hockey has a saying that describes the problem security organizations face when trying to integrate AI:"You have to skate to where the puck is going, not where it has been". Think of the modern security stack. It's a fragmented architecture built layer by layer over decades. Tools are siloed, some overlapping, some operating in black boxes, and others that no one remembers installing.

The new AI access problem: Why machine identities now drive trust in banking

In my experience working inside banks, identity security can be like plumbing: when it’s working, no one wants to talk about it. When there’s an incident, an audit, or a regulator—suddenly everyone wants to understand how it works. Artificial intelligence (AI) brings the same “no one cares until everyone does” energy, but with face-melting velocity. Today, AI is embedded across large parts of the financial services industry, and it has been around for more than 25 years.

How 1Password secures agent architectures

Since 1Password began, we have built security into the places where work actually happens. Security is not treated as an overlay or a separate workflow, we build directly into the browser, command lines, developer tools, and IDEs, where decisions are made and actions take place. We believe that if you want to improve security outcomes, you build where the work happens, making the secure path the simplest one.

Meet Seema: A Simpler Way to Understand Risk

Getting clear answers about your security risk shouldn’t require hours of manual work or deep platform expertise. Meet Seema – Seemplicity’s new AI assistant designed to translate complex remediation data into plain-spoken, actionable insights. Whether you’re a practitioner investigating a specific vulnerability, an engineer needing context on a finding, or a leader briefing on overall risk, Seema provides the clarity you need to move from data to action.

The Coming Regulatory Wave for AI Agents & Their APIs

For the past two years, the adoption of Generative AI has felt like a gold rush. Organizations raced to integrate Large Language Models and build autonomous agents to assist employees. They often bypassed standard governance processes in the name of speed and innovation. That era of unrestricted experimentation is rapidly drawing to a close. A massive regulatory wave is forming worldwide. Frameworks like the EU AI Act and the new ISO/IEC 42001 standard are forcing a corporate reckoning.

Introducing Forescout VistaroAI | The First SkillsBased Agentic AI for Cybersecurity

Meet Forescout VistaroAI, the first skills‑based agentic AI for cybersecurity. Forescout VistaroAI I thinks like a security expert, not a chatbot. It uses cybersecurity‑specific, preprogrammed skills to analyze anomalies, interpret posture changes, and automatically highlight affected assets. It eliminates the need for prompt engineering, providing role-based automation with human-in-the-loop control. The result is faster, more accurate decisions, and clearer starting points for real investigations.

AI Data Governance Framework: A Step-by-Step Implementation Guide

AI data governance is the structured framework that ensures sensitive data remains protected when artificial intelligence systems are used. Traditional data governance focuses on data at rest. It manages databases, access controls, storage policies, and compliance documentation. AI fundamentally changes the environment, and hence, understanding AI data and privacy is crucial. When organizations use large language models, AI agents, or retrieval-based systems, data flows dynamically.