Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

The rapid adoption of generative AI (GenAI) in the enterprise is introducing a new category of unmanaged risk known as shadow AI. Organizations frequently lack insight into which employees are using GenAI tools and how they are being accessed, resulting in visibility limitations, policy enforcement challenges, and increased risk of data exposure. Security teams face potential data leaks and compliance violations, while IT teams struggle to integrate GenAI usage into existing governance models.

MCP, short for Message Communication Protocol, refers to a category of protocols used for exchanging structured messages between systems or applications. It was developed primarily to meet the communication needs of early enterprise systems that required: MCP protocols are often seen in banking, insurance, healthcare, and telecom industries—sectors where many systems were developed before APIs became mainstream.

Generative AI has gone from a novelty to an essential part of daily workflows across all teams at an organization. Whether it’s ChatGPT, Microsoft Copilot, Claude, or Google Gemini, employees are using chatbots to copy, paste, summarize, and query data at a pace and scale we have never seen before. Unfortunately, data security has not been a fundamental feature of generative AI as the technology’s popularity and functionality has exploded.

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.