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Deepfakes and injection attacks (where attackers substitute fake video or audio into a system's input stream) are increasingly being used to bypass identity verification systems in critical moments like bank account opening, remote hiring, and account recovery. Traditional deepfake detection alone is insufficient because attackers can either create high-quality synthetic media or completely bypass the camera sensor using injection attacks, so organizations need to validate entire identity sessions end-to-end, including device integrity and user behavior signals, rather than just checking if a face looks real.

OpenAI negotiated with the Pentagon to use its AI systems for military purposes, while Anthropic refused and was blacklisted for rejecting two uses: domestic mass surveillance (monitoring Americans without individual consent) and lethal autonomous weapons (AI systems that can kill targets without a human making the final decision). OpenAI's CEO claimed to have found a way to maintain safety limits in the company's military contract, though the article does not detail what those specific terms are.

OwnerHunter is a system that uses large language models (AI trained on vast amounts of text) to identify who owns a website by analyzing webpage content across multiple languages. It improves on older methods that struggled when webpages listed many names or were written in non-English languages, using strategies like checking multiple sources on a page and verifying results to accurately determine the true owner.

OpenAI secured a deal with the U.S. Department of Defense after the Trump administration forced federal agencies to stop using Anthropic's AI technology, citing disagreements over how the Pentagon wanted to use the artificial intelligence startup's systems. OpenAI's CEO Sam Altman stated that his company shares the same ethical boundaries (called guardrails, which are safety limits built into AI systems) as Anthropic regarding how the technology should be used.

Anti-AI protest groups organized a march in London on February 28 with a couple hundred protesters expressing concerns about generative AI (AI systems trained on large amounts of data to generate text, images, or other content), ranging from job displacement and harmful content to existential risks. The protest represents a significant growth in organized anti-AI activism, with groups like Pause AI expanding rapidly since their 2023 founding to mobilize larger crowds around concerns that researchers have documented about AI systems like ChatGPT and Gemini.

Researchers demonstrated that LLMs (large language models, AI systems trained on vast amounts of text) can effectively de-anonymize people by identifying them from their anonymous online posts across platforms like Hacker News, Reddit, and LinkedIn. By analyzing just a handful of comments, these AI systems can infer personal details like location, occupation, and interests, then search the web to match and identify the anonymous user with high accuracy across tens of thousands of candidates.

AI is developing faster than government regulation can keep up, creating risks like chatbots giving harmful advice to teens and potential misuse for creating biological weapons. Unlike industries such as nuclear power or pharmaceuticals, AI companies are not required to disclose safety problems or undergo independent testing before releasing new models to the public. The author argues that independent oversight of AI platforms is necessary to protect people's rights and safety.

Security leaders (CISOs, who oversee an organization's security strategy) face pressure to enable innovation like AI adoption while reducing risk and staying within budget constraints. The source argues that well-governed innovation actually reduces risk by preventing uncontrolled tool sprawl and shadow IT (unauthorized software systems), but unmanaged innovation creates fragile systems that increase damage from security incidents. The key is bringing discipline to experimentation by automating routine tasks, giving teams ownership of meaningful improvements with clear end goals, and using AI strategically only where it changes the risk equation without creating new vulnerabilities.