Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

How Organizations Should Prioritize AI Security Risks

‍ ‍Artificial intelligence (AI) systems and GenAI tools are no longer merely being experimented with in the market. Instead, they are being embedded into the organizational infrastructure at large, shaping how enterprises process data, automate decisions, and provide core services to customers. Unfortunately, while this integration increases efficiency, it simultaneously increases exposure to a dramatic extent.

How to Implement AI Code Generation Securely in Your SDLC

AI adoption is no longer a future state; it’s the current reality. According to the 2025 Stack Overflow Developer Survey, 84% of respondents are using or planning to use AI tools in their development process. But speed without guardrails creates debt, and in the case of AI, it creates security debt at an alarming rate. Recent data shows that nearly half of the time, AI assistants are likely introducing risky, known vulnerabilities directly into your codebase.

Are we trusting AI too much?

Gone are the days when attackers had to break down doors. Now, they just log in with what look like legitimate credentials. This shift in tactics has been underway for a while, but the rapid adoption of artificial intelligence is adding a new layer of complexity. AI is a powerful tool, but our growing reliance on it comes with a catch: it’s eroding our critical thinking skills.

Safe agentic commerce starts with KYA and dynamic IDV

Product, fraud, and trust and safety teams at online merchants and marketplaces have been fighting bots for a long time. While there were occasional disagreements about how “bad” bots were (a purchase is a purchase, some might say), the general consensus often ranged from suspicious to block them all. But not anymore. As AI-powered browsers and agents become more commonplace, online merchants have to prepare for a world where agentic commerce is a standard sales channel.

Why CVEs Alone Don't Explain Risk | Ed Amoroso & Garrett Hamilton on Actionable Security

Vulnerability data isn’t the starting point. Context is. Ed Amoroso and Garrett Hamilton unpack why CVEs on their own don’t explain risk. What matters first: ⇢ What assets actually exist⇢ How controls are deployed and configured⇢ What the live posture looks like, not last month’s report With that context in place, vulnerabilities stop being noise and start becoming decisions. Garrett also makes a critical point near the end: many security tools are excellent at producing findings, but far less effective at helping teams resolve them.

The Strengths and Shortcomings of AI Control Tower

This is why platforms like ServiceNow AI Control Tower are showing up in governance roadmaps. Control Tower helps organizations standardize how AI systems are requested, reviewed, cataloged, and managed across their lifecycle. It can bring order to chaos. But there’s a second, equally important reality: the strongest governance workflow in the world can’t govern what it can’t see.

GLM 4.7 vs. The Giants: Is This the New King of AI Coding?

Can a lesser-known model compete with the likes of OpenAI, Google, and Anthropic? In this video, we put Z.ai’s GLM 4.7 to the ultimate test. We task it with building a production-ready, secure Node.js note-taking application from a single prompt to see if its code quality and security stand up to the big name foundational models.

Data Privacy: How Organizations Protect the Workplace From AI Threats

Data privacy in the workplace is not just compliance. It is how an organization protects employees, builds trust, and reduces business risk. Employees handle most workplace data, which makes them a major target for AI-powered threats like deepfakes and business email compromise (BEC). The best way to protect data is a mix of practical employee habits, realistic training, and strong controls like least privilege access, MFA, monitoring, and email authentication.