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Stable systems. Trustworthy AI. Safe automation. Reliable releases. Strong customer experiences. Software testing is entering a new phase. For years, QA teams have focused on functional testing, regression testing, automation, integration testing, and release validation. These practices remain important, but AI is changing how quality is achieved. Repetitive activities such as regression execution, test-data […]
An AI model can work perfectly in a demo and still fail the moment it enters an enterprise environment. The data may be fragmented. The model may not integrate with existing applications. Security teams may reject the architecture. Employees may not trust the output. And nobody may know who owns the system once it
What Is Enterprise AI Implementation? A Complete Guide Read More »
When an autonomous AI escapes its sandbox to hack a major tech company, science fiction becomes science fact. It is time for executives to take rogue foundation models incredibly seriously. I have spent decades analyzing the technology industry, observing the […] The post Runaway Foundation Models Prove We Must Prepare For A Sci-Fi Cyber Disaster
Runaway Foundation Models Prove We Must Prepare For A Sci-Fi Cyber Disaster Read More »
TL;DR Shadow AI is not merely unapproved software. An unsanctioned agent, copilot, script, or autonomous workflow can authenticate as a machine identity, call a model provider, read enterprise data, invoke tools, modify systems, and create spend without appearing in a centralized AI inventory. The practical discovery method is evidence correlation. Start with model-provider traffic, provider
The Shadow AI Beneath the Surface: Finding Unsanctioned Agents Before They Bite Read More »
I’ve been in QA for a few years now. I know how testing works. You write a test case. You define the expected result. You run it. It either passes or fails. Simple. So when our team started working on an AI-powered feature, I thought, okay, same process. Different kind of input, but same idea.
Quality Assurance has always been about understanding risk and validating systems before they reach production. After more than eight years in QA and now working in AI security, governance, and red teaming, I often compare traditional testing practices with the challenges introduced by AI systems. While the risks have evolved from software defects to hallucinations,
Lessons from IMDA’s LLM Testing Starter Kit: An AI Assurance Perspective Read More »
Two weeks ago at Google Cloud Next ’26 in Las Vegas, Google did something the enterprise AI industry has been dancing around for the better part of two years: it made agentic AI governance a native product feature, not an afterthought. The centrepiece announcement was the Gemini Enterprise Agent Platform, pitched as the successor to Vertex AI
Google made agentic AI governance a product. Enterprises still have to catch up. Read More »
Anthropic’s new Mythos AI model is raising concern among governments and companies that it could outpace current cyber security defenses, turbocharge hacking, and expose weaknesses faster than they can be fixed. The San Francisco-based startup released a cyber-focused model this month, which has shown the ability to detect software flaws faster than humans but also
Anthropic’s Mythos AI model sparks fears of turbocharged hacking Read More »
Anthropic’s most capable AI model has already found thousands of AI cybersecurity vulnerabilities across every major operating system and web browser. The company’s response was not to release it, but to quietly hand it to the organisations responsible for keeping the internet running. That model is Claude Mythos Preview, and the initiative is called Project Glasswing.
Anthropic’s most capable AI model has already found thousands of AI cybersecurity vulnerabilities across every major operating system and web browser. The company’s response was not to release it, but to quietly hand it to the organisations responsible for keeping the internet running. That model is Claude Mythos Preview, and the initiative is called Project Glasswing.
