Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Moonshot AI governance breakdown: Lessons from the Cursor/Kimi K2.5 incident

What happens when a $29 billion company forgets to rename a model ID, and what it means for every organization using open-source AI. On March 19, 2025, Cursor, the AI-powered coding tool valued at $29 billion and generating an estimated $2 billion in annual recurring revenue, launched Composer 2, its newest and most powerful coding model.

Why NER models fail at PII detection in LLM workflows - 7 critical gaps

In AI systems, PII detection is the first step. Not the most glamorous step. But the one that, when it fails, takes everything else down with it. Identifying sensitive data (names, Social Security numbers, financial records, health information) has to happen before any of it reaches an LLM. Get this wrong, and you’re looking at one of two bad outcomes: Traditional DLP systems could afford to be aggressive with detection. LLMs can’t. They depend on full context to generate correct outputs.

Data Integrity: Protecting Your Campaign's SMS Infrastructure

Every political campaign relies on getting messages out to voters fast. A solid tech setup is the backbone of your efforts. Protecting the setup means keeping your data safe and accurate. It prevents small leaks from becoming massive problems later on.

AI-Powered Freelancing Marketplace for Professionals and AI Agents

The rise of AI-powered tools has completely changed the game for freelancers. It is not just a matter of having a polished profile or a good job title anymore. The companies now focus a lot on the problem-solving skills, quick delivery capabilities of the freelancers as well as their proficiency in using various AI tools like ChatGPT, Claude, and Copilot in day-to-day work scenarios. In that context, Ugig.net: The Marketplace for AI Agents fits naturally into the conversation, because it reflects a growing demand for faster execution, clearer communication, and a smoother path from idea to completed work.

Why More AI Doesn't Guarantee Better Vulnerability Management Outcomes

AI is everywhere in vulnerability management right now. Technology vendors in all areas are adding new features and making bold claims about revolutionary capabilities. But here's the reality, especially for vulnerability and exposure management: more AI doesn't automatically mean less risk. The gap between AI's promise and its practical impact in enterprise vulnerability management is wider than most organizations realize.

Camille Stewart Gloster on how AI systems can help you wade through log data and get more done

AI and machine learning are already being used in cybersecurity to help reduce the "noise of all the indicators" that security teams receive. These systems can serve as a "first line of defense" by setting up potential response actions. However, organizations need to ensure they keep human analysts in the loop because contextual knowledge and human judgment remain critical. Data Security Decoded is available on our YouTube channel!

Thinking long-term growth in an AI-dominated industry with Stel Valavanis of onShore Networks [302]

Today we're speaking with Stel Valavanis, Founder and Chairman at onShore Networks and Co-Founder at The Gallery Building, about sustaining a security company over three decades of industry changes. We also dive into investing in start ups and how founders can think long term about governance and growth.