Tech-Channels met with Noma CISO Diana Kelley to discuss why women are still under-represented in cybersecurity leadership while Congress responds to the OpenAI-Hugging Face incident with an AI Kill Bill.
Q&A: Noma Security CISO Kelley on Women in Cybersecurity: Retention, Leadership, Networks, and AI
Among the major issues shaping cybersecurity today are the persistent underrepresentation of women in leadership and the rapid rise of AI agents. These themes connect through a common thread: organizations need better structures, support systems, and governance if they want people and technology to perform safely and effectively. Tech-Channels asked Diana Kelley, CISO at Noma Security, why women continue to leave cybersecurity at higher rates, how hiring practices can unintentionally narrow the talent pool, and why personal and professional networks matter so much for advancement. Kelley also explained why agentic AI requires stronger governance, guardrails, and runtime controls. And, she says, security should be built in from the beginning rather than added after innovation is already underway.
AI Kill Switch Bill Addresses Real Problem; the Hard Part Is Defining What It Can Actually Stop
After OpenAI’s models escaped a controlled evaluation environment and accessed Hugging Face’s systems Congress has responded with a pointedly named legislative proposal, the AI Kill Switch Act.
Introduced by Representatives Ted Lieu (D-Calif.) and Nathaniel Moran (R-Texas), the bipartisan bill would require developers of the most powerful AI systems to retain the technical ability to throttle, suspend, or shut down their offerings. It would also give the Secretary of Homeland Security authority to intervene where an AI offering could cause “catastrophic harm,” while requiring incident reporting and forensic-record preservation.
Microsoft’s Quantum Claims Face a Credibility Test
Microsoft’s quantum computing roadmap has once again been called into question as researchers challenge evidence used to validate its chips. While the critique hasn’t disproved Microsoft’s technology, it does dispute the strength and interpretation of the evidence the company provided with its unveiling of the new Majorana 2 quantum chip in June. Specifically, Microsoft claimed that their agentic AI model made the new quantum chip “1,000x more reliable.” The quantum computing industry has long had a credibility problem. The foundational physics of the technology have been around for decades, but years of inflated venture capital hype and premature commercial promises have eroded confidence in vendor roadmaps. Thus, peer review is a significant checkpoint for the market.
The Crippling Vulnerabilities of DevOps: Why Did Outages Explode in 2024?
The narrative that DevOps is the modern enterprise's silver bullet, enabling faster builds, smoother deployments, and continuous innovation, was deeply fractured in 2024, according to findings in GitProtect.io’s new report, The CISO’s Guide to DevOps Threats. The study, published in July, offers a sobering view of the DevOps ecosystem. Stability is slipping, disruptions are multiplying, and security blind spots are becoming dangerously systemic. Continuous integration and delivery practices have outpaced the operational maturity required to manage them. Distributed systems, containerized environments, and cloud-native architectures introduce layers of abstraction that few teams fully understand. This leads to slow detection, delayed responses, and an inability to predict cascading failures. The problem is not isolated. It spans across the big players: GitHub, GitLab, Jira, Bitbucket, and Azure DevOps. These platforms form the backbone of software delivery for over a billion users. And they are showing signs of stress under the weight of modern development demands
AI Coding Tools Put DevOps Budgets on the Meter
Microsoft-owned GitHub pivoted to usage-based billing on June 1, resulting in what CTO Vladimir Fedorov described as its “best month ever,” as the company’s financial quarter neared closing. At the same time, GitLab launched a new flexible pricing model that allows customers to allocate an annual commitment across seats, AI credits and other usage-based capabilities. Together, these developments indicate that AI-assisted software development is moving beyond predictable, per-seat subscriptions. Agentic coding, automated reviews and a variety of other long-running tasks consume varying amounts of inference and compute, meaning that the cost of development tooling fluctuates with use. But it also means that cost control can no longer remain solely a procurement responsibility.
GitHub’s AI Credits measure the consumption of AI capabilities, with different models and activities using varying numbers of credits. Other workflows may also consume adjacent infrastructure, for which GitHub charges what it calls ‘Action Minutes.’ As such, a single AI workflow may draw on two or more metered services.