Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

In 2025, AI is further transforming how software is built—accelerating code generation, testing, and deployment. But while it boosts speed and productivity, AI-driven development introduces new risks that developers and security teams can’t afford to ignore. To secure this next-gen development environment, organizations must understand the evolving threat landscape and adopt smarter, more integrated security strategies.

A practical guide for chief AI officers and technology leaders implementing federal AI governance The US Office of Management and Budget's recent memoranda — M-25-21, "Accelerating Federal Use of AI through Innovation, Governance, and Public Trust," and M-25-22, "Driving Efficient Acquisition of Artificial Intelligence in Government" — establish comprehensive frameworks for federal agencies that implement AI systems while maintaining appropriate safeguards.

AI security threats are evolving at roughly the same speed that AI itself is: extremely fast. One of the most recent—and least understood—vulnerabilities involves vector and embedding weaknesses. These issues have gained attention with their addition to the OWASP Top 10 for LLMs, and the risks are becoming more urgent as Retrieval-Augmented Generation (RAG) continues to dominate enterprise AI adoption.

One of the confidentiality concerns associated with AI is that third parties will use your data inputs to train their models. When companies use inputs for training, the inputs may later become or inform outputs, potentially exposing confidential information to other people.

Today during SASEfy 2025, Cato Networks announced its latest AI innovation. Cato CASB (Cloud Access Security Broker), a native feature in the Cato SASE Cloud Platform, is now enhanced with new capabilities for generative (GenAI) applications including a shadow AI dashboard and policy engine. With the shadow AI dashboard, enterprises can detect, analyze, and gain insights into the use of GenAI. With the policy engine, enterprises can take control of user activities in GenAI applications.

Artificial intelligence isn’t just a buzzword in cybersecurity—it’s rapidly becoming the backbone of both offense and defense in the digital battlefield. From hyper-realistic deepfakes to machine learning-powered threat detection, AI is fundamentally changing how we manage cyber exposure.

The web has evolved—and so have its risks. Today’s web pages are built with dozens of party scripts for ads, analytics, and dynamic features. While these improve user experience, they also open the door to cyber threats, especially when handling credit card data. As attackers increasingly target browsers rather than servers, the challenge of client-side security has grown into a critical concern for security and compliance teams.

Wallarm Research has just released a powerful new Nuclei template targeting a new kind of exposure: the Model Context Protocol (MCP). This isn’t about legacy devtools or generic JSON-RPC pinging. It’s about the protocol fueling next-gen LLM applications — and it’s already showing up exposed in the wild.

On March 25, OpenAI introduced image generation for ChatGPT-4o and ChatGPT-4o mini. On March 31, it was announced that the tool was available for free to all users. Since then, users have quickly discovered that ChatGPT’s image generator can be manipulated to create fake receipts and forge other documents.