Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

AI adoption is moving faster than most of us anticipated and, more importantly, faster than most organizations can govern it. Organizations are implementing the use of AI-enabled applications, browser extensions, coding assistants, and automated agents to enable employees to work faster. In many cases, these tools are adopted without security review, procurement approval, or a clear understanding of where organizational data is being sent.

If you’re evaluating API discovery tools right now, you’ve probably already seen a handful of demos that look nearly identical: a clean dashboard, an inventory count, maybe a risk score. What’s harder to see in a 30-minute demo is whether that inventory reflects what’s actually running in production, or just what the vendor’s connectors happened to catch on setup day.

The shift to cloud-native infra has broken the traditional perimeter security model. Modern cloud environments are dynamic, heavily distributed, and identity-driven, creating security challenges that conventional security tools were never built to address. Traditional SIEM and vulnerability management tools lack native capabilities to detect issues in IAM policies, S3 bucket ACLs, or the blast radius of a misconfigured Kubernetes node pool.

NIST AI RMF and ISO/IEC 42001 answer different questions, so the choice is rarely about which one is better. One gives you a risk process your engineering teams can run. The other gives you a management system an auditor can certify. Organizations that treat them as rival options usually pick the wrong one for the problem in front of them. ‍

Directors ask for AI risk reporting because oversight failure is personally actionable. Under the Caremark line of cases, a board that cannot demonstrate it monitored a material risk carries exposure of its own, and AI has moved into that category for most enterprises. The request is rarely curiosity about the technology. ‍ The framing determines what belongs in the pack.

Most vendor onboarding and app access work is waiting and follow-ups. Waiting for someone to notice a form came in, assign a tier, chase a questionnaire, or dig up the context behind a Slack request. Risk Automations workflows remove that wait and automate the follow-up. A trigger fires, and the workflow runs to a concrete outcome: a ticket created, a message sent, a risk tier assigned. To make those workflows easier to launch, Risk Automations includes an ever-expanding template library.

CVE-2026-34265 is a memory corruption vulnerability in the Application Server ABAP component of SAP NetWeaver and the ABAP Platform. The flaw stems from logical errors in how the SAP kernel parses DIAG protocol messages, the proprietary protocol that carries traffic between the SAP GUI presentation layer and the application server. Malformed input reaching the parser leads to an out-of-bounds write, classified as CWE-787. The vulnerability carries a CVSS v3.1 base score of 9.8 (Critical).