Reading view

Check Point Secures AI Factories with NVIDIA

As businesses and service providers deploy AI tools and systems, having strong cyber security across the entire AI pipeline is a foundational requirement, from design to deployment. Even at this stage of AI adoption, attacks on AI infrastructure and prompt-based manipulation are gaining traction. Per a recent Gartner report, 32% of organizations have already experienced an AI attack involving prompt manipulation, while 29% faced attacks on their GenAI infrastructure in the past year. Nearly 70% of cyber security leaders said emerging GenAI risks demand significant changes to existing cyber security approaches. And a recent Lakera survey found that only 19% of organizations […]

The post Check Point Secures AI Factories with NVIDIA appeared first on Check Point Blog.

  •  

Artificial Intelligence, Copyright, and the Fight for User Rights: 2025 in Review

A tidal wave of copyright lawsuits against AI developers threatens beneficial uses of AI, like creative expression, legal research, and scientific advancement. How courts decide these cases will profoundly shape the future of this technology, including its capabilities, its costs, and whether its evolution will be shaped by the democratizing forces of the open market or the whims of an oligopoly. As these cases finished their trials and moved to appeals courts in 2025, EFF intervened to defend fair use, promote competition, and protect everyone’s rights to build and benefit from this technology.

At the same time, rightsholders stepped up their efforts to control fair uses through everything from state AI laws to technical standards that influence how the web functions. In 2025, EFF fought policies that threaten the open web in the California State Legislature, the Internet Engineering Task Force, and beyond.

Fair Use Still Protects Learning—Even by Machines

Copyright lawsuits against AI developers often follow a similar pattern: plaintiffs argue that use of their works to train the models was infringement and then developers counter that their training is fair use. While legal theories vary, the core issue in many of these cases is whether using copyrighted works to train AI is a fair use.

We think that it is. Courts have long recognized that copying works for analysis, indexing, or search is a classic fair use. That principle doesn’t change because a statistical model is doing the reading. AI training is a legitimate, transformative fair use, not a substitute for the original works.

More importantly, expanding copyright would do more harm than good: while creators have legitimate concerns about AI, expanding copyright won’t protect jobs from automation. But overbroad licensing requirements risk entrenching Big Tech’s dominance, shutting out small developers, and undermining fair use protections for researchers and artists. Copyright is a tool that gives the most powerful companies even more control—not a check on Big Tech. And attacking the models and their outputs by attacking training—i.e. “learning” from existing works—is a dangerous move. It risks a core principle of freedom of expression: that training and learning—by anyone—should not be endangered by restrictive rightsholders.

In most of the AI cases, courts have yet to consider—let alone decide—whether fair use applies, but in 2025, things began to speed up.

But some cases have already reached courts of appeal. We advocated for fair use rights and sensible limits on copyright in amicus briefs filed in Doe v. GitHub, Thomson Reuters v. Ross Intelligence, and Bartz v. Anthropic, three early AI copyright appeals that could shape copyright law and influence dozens of other cases. We also filed an amicus brief in Kadrey v. Meta, one of the first decisions on the merits of the fair use defense in an AI copyright case.

How the courts decide the fair use questions in these cases could profoundly shape the future of AI—and whether legacy gatekeepers will have the power to control it. As these cases move forward, EFF will continue to defend your fair use rights.

Protecting the Open Web in the IETF

Rightsholders also tried to make an end-run around fair use by changing the technical standards that shape much of the internet. The IETF, an Internet standards body, has been developing technical standards that pose a major threat to the open web. These proposals would give websites to express “preference signals” against certain uses of scraped data—effectively giving them veto power over fair uses like AI training and web search.

Overly restrictive preference signaling threatens a wide range of important uses—from accessibility tools for people with disabilities to research efforts aimed at holding governments accountable. Worse, the IETF is dominated by publishers and tech companies seeking to embed their business models into the infrastructure of the internet. These companies aren’t looking out for the billions of internet users who rely on the open web.

That’s where EFF comes in. We advocated for users’ interests in the IETF, and helped defeat the most dangerous aspects of these proposals—at least for now.

Looking Ahead

The AI copyright battles of 2025 were never just about compensation—they were about control. EFF will continue working in courts, legislatures, and standards bodies to protect creativity and innovation from copyright maximalists.

  •  

Understanding global AI diffusion

Artificial intelligence is transforming the way we work, learn, and innovate—and it’s doing so at a pace that surpasses every major technology before it. Microsoft’s inaugural AI Diffusion Report offers a comprehensive look at how AI adoption is accelerating worldwide, drawing on data from more than 100 countries. In less than three years, more than 1.2 billion people have used AI tools, a rate of adoption faster than the internet, the personal computer, or even the smartphone. This rapid diffusion underscores AI’s potential as a general-purpose technology but also highlights the urgent need to ensure equitable access.

The report introduces three indices—the AI Frontier Index, the AI Infrastructure Index, and the AI Diffusion Index—to help policymakers, researchers, and industry leaders understand where breakthroughs are happening, where capacity exists to scale, and where AI is being used to improve lives. These insights show that adoption is fastest where connectivity and digital infrastructure are strongest, while nearly four billion people still lack the basics needed to participate in the AI economy. Bridging this gap is essential to avoid deepening global divides.

Beyond the numbers, the report illustrates the need for collaborative action to expand access to digital infrastructure, strengthen skills development, and promote responsible AI policies. By investing in these foundational elements, governments and organizations can unlock AI’s potential for growth and innovation. The data makes clear that speed alone does not guarantee shared prosperity—broad accessibility does.

To explore the full findings and recommendations, read the AI Diffusion Report.

The post Understanding global AI diffusion appeared first on Microsoft On the Issues.

  •  

Getting Started with AI Hacking Part 2: Prompt Injection

In Part 2, we’re diving headfirst into one of the most critical attack surfaces in the LLM ecosystem - Prompt Injection: The AI version of talking your way past the bouncer.

The post Getting Started with AI Hacking Part 2: Prompt Injection appeared first on Black Hills Information Security, Inc..

  •  

Augmenting Penetration Testing Methodology with Artificial Intelligence – Part 1: Burpference

Burpference is a Burp Suite plugin that takes requests and responses to and from in-scope web applications and sends them off to an LLM for inference. In the context of artificial intelligence, inference is taking a trained model, providing it with new information, and asking it to analyze this new information based on its training.

The post Augmenting Penetration Testing Methodology with Artificial Intelligence – Part 1: Burpference appeared first on Black Hills Information Security, Inc..

  •  

Getting Started with AI Hacking: Part 1

Getting Started with AI Hacking

You may have read some of our previous blog posts on Artificial Intelligence (AI). We discussed things like using PyRIT to help automate attacks. We also covered the dangers of […]

The post Getting Started with AI Hacking: Part 1 appeared first on Black Hills Information Security, Inc..

  •  

Pitting AI Against AI: Using PyRIT to Assess Large Language Models (LLMs) 

Many people have heard of ChatGPT, Gemini, Bart, Claude, Llama, or other artificial intelligence (AI) assistants at this point. These are all implementations of what are known as large language […]

The post Pitting AI Against AI: Using PyRIT to Assess Large Language Models (LLMs)  appeared first on Black Hills Information Security, Inc..

  •  
❌