Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Shadow AI refers to the unauthorized or unmanaged use of AI tools, models, frameworks, APIs or platforms within an organization, operating outside established governance frameworks. While employees may adopt these AI tools with good intentions, seeking to enhance productivity or solve problems more efficiently, the lack of oversight creates significant security, compliance, and operational risks.

Third-party cyber risk management (TPCRM) has emerged as a critical discipline, moving beyond traditional approaches to address the unique and evolving cyber threats posed by vendor relationships. This post explains the core tenets of TPCRM, outlines key requirements for ideal tools, and suggests implementation strategies for this new, important branch of cybersecurity.

While we talk a lot on this site about the US Government’s various cybersecurity frameworks, like FedRAMP and CMMC, there’s one significant framework that deserves just as much attention: ISO 27001. ISO 27001, being an ISO standard, is an international framework for cybersecurity divorced from any one country’s government.

Threat Intelligence (TI) has become the secret weapon of modern security teams—essential for identifying possible emerging threats before they escalate. But TI is only as valuable as its accuracy, relevancy, and timeliness. Unfortunately, many traditional TI approaches can no longer keep up, as security teams are plagued with information overload: too many signals, too little context, and limited resources to process everything. This is why the coupling of GenAI and TI is a game changer.

Your AI Agent just updated a vendor’s payment details in your Enterprise Resource Planning (ERP) system based on a seemingly harmless prompt. No data was exfiltrated. No access policy was violated. But now, a $250,000 payment is sitting in a fraudulent bank account. This is the new face of AI risk. As enterprises adopt AI Agents - either off the shelf or custom built, security teams are facing a fast-moving shift.

For years, vulnerability scanners have been the cornerstone of enterprise security programs. But as organizations scaled, and as infrastructure, applications, and attack surfaces diversified, the single-scanner model broke down. Security teams now face a fragmented reality. Data pours in from dozens of sources: endpoint detection tools, cloud security platforms, application security testing, and more. Each of these systems generates findings with its own schema, priorities, and assumptions. The result?

How many times have you experienced this scenario at work? A hot new AI tool emerges, promising game-changing productivity and innovative features. You can’t wait to try it out in your own workflow. But what is often your security team’s first instinct? Block it.