Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Artificial intelligence (AI) is transforming industries, but it also introduces new risks that organizations must manage carefully. This article explains how to develop and apply AI risk categories aligned with recognized frameworks, focusing on operational, technical, and ethical risks. Readers will learn how to prioritize these risks based on their potential impact on the organization.

Infrastructure as a service (IaaS) is becoming a bigger opportunity for managed service providers (MSPs), cloud service providers (CSPs), hosters and telecommunications providers. Clients still need compute, storage and networking. But many are rethinking where those workloads should run.

OWASP, the Open Worldwide Application Security Project, has published Top 10 lists for over two decades to help security teams prioritize the risks that matter most. The original OWASP Top 10 for web applications became the industry’s default checklist for application security. When large language models moved into production, OWASP followed with the Top 10 for LLM Applications, addressing risks like prompt injection and sensitive information disclosure in single-turn model responses.

Every security leader has a version of the network in their head. They know which systems should be segmented, which applications should be reachable, which ports should never be open, and which access paths should not exist. They know how the architecture is supposed to work. The harder question is whether the live environment is actually enforcing that design right now. That question is getting more difficult to answer.

If you’re running applications behind an Application Load Balancer, Amazon CloudFront, API Gateway, or AWS AppSync, AWS WAF is your first line of defense against malicious web traffic. Graylog provides a purpose-built way to make those enforcement decisions immediately actionable.

TL;DR: AI governance solutions help organizations inventory, secure, and monitor AI systems. Best for AI security and shadow AI: Mend AI; enterprise risk and compliance: Credo AI and IBM watsonx.governance; model monitoring: Fiddler AI. Effective AI governance implementation involves establishing a cross-functional committee, compiling an AI bill of materials (AI-BOM) to identify risks, and implementing policies based on frameworks like NIST AI RMF.

For two decades, “penetration testing” has meant the same thing: once a year, you hire a firm, a human tester spends a week or two on your systems, and you get a PDF. Most compliance frameworks were written around exactly that ritual, a slow, manual, point-in-time engagement. Software doesn’t ship once a year anymore. It ships many times a day.