AI Risk Management

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AI Is Coming for Inefficiency: How Enterprise Leaders Should Redesign Work Before Automating It

AI Is Coming for Inefficiency: How Enterprise Leaders Should Redesign Work Before Automating It AI is not just another technology wave waiting for a procurement cycle, a license rollout, and a few enablement sessions. It is a pressure test on the way work actually moves through the enterprise. That is why Gartner’s framing matters. The […]

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IMDA

Lessons from IMDA’s LLM Testing Starter Kit: An AI Assurance Perspective

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,

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From Traditional QA to AI Assurance and Governance

Breaking the Black Box: From Traditional QA to AI Assurance and Governance

I recently had the opportunity to review IMDA’s Starter Kit for Testing LLM-Based Applications for Safety and Reliability. As someone who has spent over 14 years in Quality Assurance, I was curious to see how established testing principles are being adapted to address the unique challenges introduced by Large Language Models (LLMs). What I expected

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Breaking the Black Box: From Traditional QA to AI Assurance and Governance

Testing for Trust: What IMDA’s LLM Testing Starter Kit Teaches Us At Spritle About AI Assurance

Breaking the Black Box: From Traditional QA to AI Assurance and Governance I recently had the opportunity to review IMDA’s Starter Kit for Testing LLM-Based Applications for Safety and Reliability. As someone who has spent over 14 years in Quality Assurance, I was curious to see how established testing principles are being adapted to address

Testing for Trust: What IMDA’s LLM Testing Starter Kit Teaches Us At Spritle About AI Assurance Read More »

AI Risk & Compliance in 2026: Why QA Teams Must Lead the Shift

Artificial Intelligence is no longer a futuristic concept or an experimental capability. In 2026, AI has firmly embedded itself into core business operations—powering decisions in hiring, finance, healthcare, customer experience, and beyond. This shift brings a fundamental change: AI risk is now business risk. For Quality Engineering teams, especially QA leaders, this marks a turning

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