Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Two years ago, your teams shipped software. Today they ship two different things. They ship software that AI mostly wrote. And they ship AI systems they built themselves: models, agents, features that reason and act. Most security programs are still scoped for the first and blind to the second. That gap is not a tooling problem. It is a category problem. And the way the industry is drawing the categories is making it worse.

Microsoft’s approach of generative artificial intelligence has fundamentally redefined corporate productivity. The "Copilot" brand has become synonymous with workplace efficiency, promising to accelerate everything from writing software to summarizing executive board meetings. For a security analyst, however, this widespread integration introduces significant challenges to the attack surface they manage.

The writing is on the wall: artificial intelligence has moved past the experimental phase and has cemented its place as a core component of the modern enterprise stack. For CISOs, the playbook of flat firewall blocking is ineffective—bans don’t halt adoption, they simply drive usage underground into unmanaged shadow streams. To protect corporate assets without stalling business velocity, security leaders are seeing the need to shift from blind obstruction to active, structured guidance.

CISOs and security analysts understand that the narrative surrounding artificial intelligence risk has changed. The old assumption that AI risk begins and ends with an employee copying and pasting a sensitive paragraph into a public ChatGPT prompt has dissipated, and we now see that AI has rapidly transitioned from an occasional consumer novelty into a deeply embedded, departmental infrastructure.

Anthropic released a pre-built KYC Screener agent last month. It runs a four-step workflow on onboarding records to extract structured data from KYC documents, evaluate that data against a firm's KYC rules, screen named parties, and escalate exceptions to a compliance file for human review. The Anthropic template is purpose-built for meeting basic KYC compliance requirements during onboarding, and it lowers the cost of getting it right.

Artificial intelligence (AI) is transforming businesses worldwide, offering powerful tools to automate, analyze, and innovate. Yet, with this power comes significant risk. Organizations must implement AI governance frameworks that map, measure, and manage AI risks continuously. ‍ This article explains how effective AI governance helps prioritize risks aligned with business goals, enabling companies to mitigate threats before they escalate.

As employees increasingly rely on AI tools and AI agents in daily workflows, organizations are facing a new workforce security challenge: how to reduce risk without slowing productivity. Security leaders are no longer just protecting systems and identities. They also need to manage how employees interact with AI-generated content, automation, and decision support tools.

With over 48,000 CVEs published in 2025 and attackers weaponizing vulnerabilities in as little as 20 hours, traditional vulnerability management is no longer enough. This post breaks down the key findings from the SANS whitepaper The Exposure Gap: From Vulnerability Management to AI-Driven Control, and what it means for security teams trying to get ahead of risk. In 2025, over 48,000 CVEs were published. That’s roughly 130 new vulnerabilities every single day.