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 was a highly technical guide focused on AI models and algorithms. What I found instead was a practical framework that felt surprisingly familiar from a QA perspective. While the technology may be evolving rapidly, the core objective remains unchanged: building confidence that a system behaves safely, reliably, and as intended.
One statement from the document captured this idea perfectly:
“Testing and assurance play a critical role in a trusted AI ecosystem.”
That message is woven throughout the guide and, in many ways, reflects how the role of quality professionals is evolving today.
Quality Risks Have Changed, The Need for Testing Hasn’t
One of the first things that stood out was the way the document categorizes the most common risks associated with LLM-based applications:
· Hallucination and Inaccuracy
· Bias in Decision Making
· Undesirable Content
· Data Leakage
· Vulnerability to Adversarial Prompts
At first glance, these may appear to be entirely new challenges. However, while reading through the explanations and examples, I found myself drawing parallels with risks QA teams have always managed.
We’ve always worked to prevent incorrect system behavior. Today, AI introduces the possibility of hallucinations and fabricated responses.
We’ve always considered security and misuse scenarios. Now, prompt injection and adversarial attacks become part of the threat landscape.
We’ve always cared about compliance and privacy. Data leakage through AI-generated responses is simply a modern extension of that concern.
The terminology may be changing, but the discipline of identifying, assessing, and mitigating risk remains at the heart of quality assurance.
A Good Test Strategy Starts Long Before Test Execution
Another aspect I loved was the emphasis on preparation before testing begins.
The guide spends considerable time discussing how organizations should identify relevant risks, define testing objectives, select representative datasets, and establish meaningful thresholds before running evaluations.
As QA professionals, we know that the success of testing is often determined long before execution starts. Poorly defined objectives, weak test data, or unclear acceptance criteria can undermine even the most rigorous test cycles.
I particularly liked the characteristics outlined for a “good” test dataset. The document emphasizes that datasets should be representative of the application’s purpose, cover realistic user interactions, and include sufficient breadth and depth to uncover meaningful issues.
This mirrors the same principles we apply when designing effective test coverage for traditional applications.
Another valuable takeaway was the recommendation to define thresholds before testing begins. The document highlights the importance of avoiding “moving the goalposts” after results are known-a principle that resonates strongly with anyone who has worked in quality management.
When Accuracy Alone Doesn’t Tell the Whole Story
The sections discussing metrics and evaluators were particularly interesting because they challenge a common assumption that accuracy alone defines quality.
The guide explains that metrics should align not only with technical objectives but also with business and policy objectives. Depending on the use case, organizations may prioritize accuracy, fairness, safety, precision, recall, or other measures.
This is an important reminder that quality is contextual.
I also appreciated the balanced discussion around evaluators. The document compares rule-based approaches with LLM-as-a-Judge methods and openly acknowledges the strengths and limitations of each.
One observation I strongly agreed with was the recommendation that automated evaluations should supplement-not replace-human judgment, particularly in high-stakes environments.
As testing professionals, we’ve always relied on tools to improve efficiency, but accountability ultimately remains with people. The same principle applies here.
Testing the Unknown: Why Red Teaming Matters
Among all the sections, the discussion on red teaming was probably the most thought-provoking.
The document makes a clear distinction between benchmarking and red teaming. Benchmarking helps validate known scenarios, while red teaming helps uncover blind spots, edge cases, and unexpected behaviors.
As I read this section, I couldn’t help but think of exploratory testing.
Traditional test cases validate expected outcomes. Exploratory testing helps uncover the issues we didn’t think to document.
Red teaming appears to play a similar role for AI systems.
The guide discusses adversarial prompts, multi-turn interactions, social engineering techniques, and other methods that can expose weaknesses in AI behavior. It also emphasizes the importance of involving diverse participants and domain experts to increase the likelihood of uncovering risks that might otherwise remain hidden.
The practical examples included in the document helped reinforce why structured red teaming is becoming an important complement to conventional testing approaches.
From Defect Prevention to Trust Validation
Perhaps my biggest takeaway from the document is that AI testing is not simply another testing specialization.
Many of the activities described-risk prioritization, threshold setting, governance decisions, human oversight, and result interpretation-extend beyond engineering teams.
They require collaboration between business stakeholders, product owners, compliance teams, security specialists, and quality professionals.
This is where I see an interesting opportunity for experienced QA practitioners.
For years, our focus has been on preventing defects and validating requirements. Increasingly, we are also being asked to help organizations understand risk, establish confidence, and determine whether systems can be trusted.
That shift feels less like a change in tools and more like an evolution of the profession itself.
Final Thoughts: Beyond Testing, Towards AI Assurance
One of the strongest messages I took away from IMDA’s Starter Kit is that AI assurance is rapidly becoming a business capability, not just a testing activity.
As organizations adopt LLM-powered applications, success will no longer be measured solely by feature delivery or model performance. It will increasingly depend on how confidently organizations can explain, govern, monitor, and trust AI-driven decisions.
At Spritle, we believe this shift requires QA teams to evolve into strategic partners in AI adoption. The future of quality lies not only in validating outputs but also in understanding system behaviour, identifying risks early, and helping establish guardrails that enable responsible innovation.
Frameworks like IMDA’s provide an excellent foundation, but their real value comes from operationalizing them within enterprise environments. That means combining engineering discipline, domain expertise, governance practices, and continuous assurance into a repeatable approach.
As AI systems become more capable, the question organizations will ask is not simply “Does it work?“
It will be:”Can we trust it at scale?“
Helping answer that question is where we see the future of Quality Engineering—and a direction we are actively investing in at Spritle.
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