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Artificial Intelligence is no longer confined to labs or bleeding-edge startups.

In 2025, AI systems are everywhere: inside SaaS products, embedded in desktop and mobile apps, powering customer support tools, driving cars, running on wearables, and even monitoring vital signs in medical devices. But the sophistication of AI doesn’t remove the need for robust testing and QA: it drastically increases it.

If you’re shipping AI-powered features, the testing principles you know and love still apply. But the execution? That needs a serious upgrade.

If you’re a software tester, QA engineer, or SDET starting to work with AI systems, this guide is for you. In it, we present the top ten most important testing practices and principles to start incorporating into your QA workflows for when testing AI systems.

While the list shouldn’t be read as a top ten, and isn’t presented in order of importance or priority, it does represent a synthesis of the most prominent, frequently discussed, and most emphasised practices based on our survey of the relevant literature. Links to relevant sources are provided throughout and at the end of the article, for further reading.

1. Trust, Risk, and Security Management (TRiSM) – Your New Testing in AI North Star

Remember when we first started talking about “shift-left” testing?

TRiSM is the AI equivalent. It’s about baking risk management into every layer of your AI system from day one. Think of it as the intersection of where explainability meets security meets governance, all wrapped up in a handy framework for delivery teams.

What makes TRiSM different from traditional risk management? AI doesn’t just break when something goes wrong. It may degrade gracefully, lie convincingly, or reinforce hidden biases – often without triggering a single alert. In healthcare, finance, or a regulated industry, these aren’t just bugs: they’re lawsuits waiting to happen.

The testing approach needs to evolve accordingly. Instead of just validating outputs, we’re testing for trustworthiness. Can stakeholders understand why the system made a decision? Is personal data protected? What happens when the model encounters something it wasn’t trained for?

How to apply TRiSM in AI testing:

• Start treating your AI model like any other critical system component, but with a risk profile that’s uniquely its own.
• Build test plans that cover not just functional accuracy, but traceability, fallback behaviours, and ethical edge cases.
• Use frameworks like STRIDE, but adapt them for LLMs and agent systems.
• Most importantly, work cross-functionally. This isn’t a software testing problem the QA team can solve in isolation.

2. Security Testing Gets Serious – Welcome to Red Teaming

If you thought API security testing was complex, wait until you meet prompt injection attacks. Traditional security testing assumes predictable inputs and outputs. AI systems are designed to be creative, which makes them scarily exploitable in ways that may not occur to a conventional penetration tester.

Red teaming — a term borrowed from military exercises where teams actively try to break systems — has become essential for testing AI. We’re talking about adversarial prompts designed to make models leak training data, jailbreak prompts that bypass safety guardrails, and social engineering attacks that manipulate AI responses for malicious purposes.

The scary part isn’t just that these attacks work – it’s that they often work silently. Your DevOps monitoring might show everything running normally while an attacker systematically extracts sensitive information or steers your AI into providing harmful advice.

How to upgrade security testing for AI systems:

• Expand your definition of security testing. Yes, validate your API access controls and rotate your secrets. But also fuzz your LLM endpoints, simulate adversarial scenarios, and document what makes your specific AI vulnerable. Every model has its own attack surface, and discovering yours ahead of time beats letting customers find it first.
• Work with your security team to run controlled adversarial simulations during pre-production cycles.
• Build up your own attack taxonomy over time – it becomes both your testing playbook and your defense strategy.

3. Data Privacy Isn’t Just Compliance Theatre Anymore

GDPR style opt-ins won’t save you when your AI model can be tricked into regurgitating training data or inferring sensitive information from seemingly innocent queries. Unlike traditional systems where data flows are predictable, AI models can unintentionally reveal personal information, make assumptions based on protected characteristics, or expose retained traces of PII in ways that aren’t immediately obvious.

This puts QA teams in an interesting position. We’re often the last line of defence before production, and if a clever prompt can extract someone’s medical information or internal documents through your AI, and your test coverage missed it, that’s a data breach with your name on it.

How to improve privacy testing around AI:

• The testing strategy needs to be proactive. Map your system’s data flow end-to-end, then build tests that stress those boundaries.
• Try prompts that attempt to infer hidden user attributes.
• Simulate access from different user roles and privilege levels.
• Validate that anonymised data stays anonymous even under repeated or sophisticated queries.
• Work with your data engineering team to identify potential leakage paths, especially the non-obvious ones.
• Consider differential privacy techniques or using synthetic data for your testing environments.

Privacy testing for AI is still an emerging discipline, but product and QA teams are uniquely positioned to lead the charge.

4. Peering into the Black Box – Explainability and Transparency

Many of us have encountered situations where the opacity of an AI system has led to a sub-optimal outcome in the wild. If you’re working in a regulated industry or involved in making high-stakes decisions, “the AI said so” isn’t going to be an acceptable explanation when things go wrong. Explainability isn’t necessarily about cracking the AI black box completely open though: it’s about creating enough transparency to understand and justify decisions.

When your AI refuses a loan application, flags a transaction as fraudulent, or recommends a treatment plan, stakeholders need to understand the reasoning. Not the mathematical internals necessarily, but the logical path that led to the decision.

How to test for explainability in AI systems:

• From a testing perspective, this means validating not just what the model does, but why it did it.
• Work with your development team to enable decision logging, use interpretability tools like SHAP or LIME for local explanations, and ensure that system outputs include reasoning where appropriate. Especially for customer-facing applications where your support team may be asked to provide those explanations.
• Treat missing or incoherent explanations as test failures.

In high-trust environments, unexplained accuracy is still a risk. The goal isn’t perfect transparency; it’s defensible transparency.

5. Governance That Actually Works in Practice

AI governance frameworks are multiplying faster than Kubernetes distributions, but most read like academic papers rather than operational guidance. The EU AI Act, NIST AI RMF, and various industry standards all have one thing in common: they assume someone is actually testing whether their requirements can be met in practice.

That someone is frequently a quality engineer or QA team.

Unlike traditional software that behaves predictably once deployed, AI systems can drift, evolve, or behave differently based on new data or changing environments. If governance mechanisms aren’t validated through testing, they become expensive documentation that nobody trusts when problems arise.

How to test governance of AI systems:

• Build test plans that verify your governance controls actually work. Can you reproduce a decision made last month? Are model versions properly tracked? Do AI outputs align with your internal policies and industry standards?
• Test your audit trails, validate role-based access controls, and simulate model rollback scenarios.
• Most importantly, get involved when governance policies are being written. You’ll be the one testing whether they’re realistic or just aspirational.

6. Humans in the Loop Aren’t Optional

No matter how sophisticated your AI becomes, human oversight remains critical—especially for decisions that affect real people. Human-in-the-Loop (HITL) design isn’t about mistrust (though a healthy dose of skepticism is implied!); it’s about recognising that AI systems fail in subtle, convincing ways that automated checks and even humans can miss.

The challenge is that AI doesn’t fail obviously. It hallucinates with confidence, reinforces problematic patterns, or slowly drifts from its original intent. Without human checkpoints, these issues compound silently until they become major problems.

How to test for HITL:

• From a testing standpoint, validate that human intervention is possible, accessible, and clearly designed to support effective decision-making. Can users flag incorrect AI outputs? Can moderators pause or override decisions in real-time? Are those signals being captured and fed back into the system for improvement?
• Design test cases around critical review points, especially in workflows where AI actions have legal, financial, or reputational consequences.
• When you find gaps in the human feedback loop, flag them as high-priority issues. Human collaboration isn’t a nice-to-have safety net. It’s a core system requirement.

7. Advanced Testing for Non-Deterministic Systems

Traditional test automation assumes deterministic behaviour – same input, same output. AI systems laugh at that assumption. They’re probabilistic, contextual, and generally non-deterministic by design. Testing them requires evolving beyond fixed test cases toward more sophisticated approaches.

Metamorphic testing, for example, focuses on relationships between inputs and outputs rather than exact values. AI-assisted test generation can help uncover edge cases that manual test design misses. Self-healing test suites can adapt as the system evolves, maintaining coverage even as the underlying AI model changes.

This isn’t just about automation anymore. It’s about intelligent automation that can keep pace with intelligent systems.

How to test non-deterministic systems like AI:

• Start by integrating AI-powered tools into your testing stack. Use LLMs to generate prompt variations for semantic testing.
• Build metamorphic relationships into your test oracles (if input X produces output Y, then similar input Z should produce similar output W).
• Monitor how your test cases perform over time and implement triggers for updating or retraining your test suite.

The goal isn’t to eliminate human judgment from testing. It’s to augment human insight with an exploration of the problem space that scales with your AI system.

8. Data Quality Determines Everything Else

Your AI system is fundamentally limited by the data it was trained on. Biases, gaps, and quality issues in training data don’t just affect accuracy, they create systematic blind spots that can lead to discriminatory outcomes or unpredictable behaviour for underrepresented groups.

QA teams should be the first to notice when systems work brilliantly for some users and fail mysteriously for others. Maybe your chatbot handles American English perfectly but struggles with other variants. Perhaps your recommendation engine consistently underserves certain demographic groups. These aren’t random edge cases, they’re symptoms of data quality problems.

How to test for bias and data diversity:

• Test data-diversity like any other functional requirement. Create test sets that represent different demographics, languages, and use cases. Build fairness assertions into your test oracles. Not just “was the answer correct?” but “was it equally correct across different contexts?”
• Use bias detection tools to quantify disparities in system performance. Push back on training or test data which lacks transparency about its sources, composition, or known limitations.
• Don’t just test the model. Validate the worldview encoded in its training data.

9. Model Lifecycle Management: CI/CD for AI

If you’ve implemented CI/CD for traditional applications, ModelOps is the equivalent discipline for AI systems. It’s about managing the complete lifecycle of models, from development through deployment, monitoring, retraining, and eventual retirement.

Models don’t stay static after deployment. They drift as data patterns change, degrade as the world evolves around them, and sometimes need complete retraining. Without strong model lifecycle management, your test results can’t be fully trusted, because you might not know which model version you actually ran them against, or whether that version reflects what’s live in production.

How to validate AI model lifecycle management:

• Make model lifecycle validation part of your standard testing practice.
• Verify that model versions are tracked and deployed predictably.
• Test rollback procedures; can you safely revert to a previous version if the latest one misbehaves? Validate that monitoring and alerting systems actually trigger when drift or other anomalies occur.

Treat model versioning with the same rigour you’d apply to application versioning. You wouldn’t ship code without CI/CD. Don’t ship models without ModelOps.

10. Continuous Monitoring: Testing Never Ends

Traditional software testing has a clear endpoint: you test, you ship, you move on to the next release. AI systems don’t follow that pattern. They continue learning, evolving, and potentially failing in new ways long after deployment. Continuous monitoring therefore becomes essential for maintaining the integrity of your AI system.

QA in AI isn’t just about verifying what’s built. It’s about tracking how the system evolves once it’s live. Your AI system will encounter novel inputs, shifting user behaviour, and new attack vectors in production. A model that performed beautifully in staging can start failing subtly once real users begin interacting with it – not because of a bug, but because the world changed.

How to build continuous improvement into AI testing:

• Build test cases that expect and validate change.
• Simulate performance degradation to test your drift detection systems.
• Verify that alerting rules actually fire when they should.
• Partner with product and data teams to make sure post-deployment metrics are being collected, reviewed, and acted upon.
• Document failures as learning opportunities and feed them back into new test scenarios. This isn’t maintenance work: it’s active quality evolution.

Continuous testing is already part of the QA mindset, but with AI, it’s now a non-negotiable discipline.

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The practices and principles we’ve outlined above reflect the current state of the art as to how AI systems should be tested, as of right now. They’ll likely change very soon, but at the time of writing they:

• Align with regulatory expectations (EU AI Act, NIST AI RMF, ISO/IEC 42001).
• Reflect best industry practices (OpenAI, Google, Anthropic, Microsoft).
• Are grounded in actionable risk mitigation and tool usage.
• Go beyond traditional software testing paradigms, stretching the limits of modern software testing principles and practices.

For ease of use, we’ve summarised them in the table below:

Practice	What It Is	Why It Matters	Skills Needed	Example Tools
Trust, Risk, and Security Management (TRiSM)	An integrated framework covering explainability, ModelOps, security, privacy, and governance	Mitigates risks, ensures robustness and responsible AI deployment	AI ethics, compliance, security, governance	Azure TRiSM, Google Responsible AI, IBM AI Governance
Comprehensive Security Testing, Including Red Teaming	Simulates adversarial attacks to expose vulnerabilities	Strengthens defences and protects systems from real-world threats	Cybersecurity, API security, adversarial testing	Burp Suite, OWASP ZAP, LangChain Guardrails
Robust Data Privacy and Protection	Applies data anonymisation, minimisation, and encryption	Protects sensitive data and ensures legal compliance (GDPR, HIPAA)	Data privacy, cryptography, secure architecture	Homomorphic encryption, Google DLP, AWS Macie
Explainability and Transparency	Provides interpretable reasoning behind AI decisions	Supports bias detection, accountability, and user trust	XAI frameworks, model interpretation, data viz	SHAP, LIME, Chain-of-Thought, ELI5
Strong AI Application Governance and Compliance	Manages AI within legal and ethical boundaries	Ensures auditability, reduces risk, supports regulatory alignment	AI governance, legal compliance, audit management	NIST AI RMF, EU AI Act tools, governance boards
Human Oversight and Collaboration (Human-in-the-Loop)	Involves human validation in AI workflows	Prevents critical failures, promotes ethical decision-making	UX design, HITL systems, ethical oversight	Snorkel, Labelbox, review dashboards
Advanced and AI-Powered Testing Methodologies	Employs AI-driven tools and techniques like metamorphic testing	Enhances test efficiency, catches edge cases and logic flaws	Test automation, AI/ML, defect prediction	Testim, Applitools, MetaMorph, ChatGPT
Quality, Diversity, and Ethical Sourcing of Data	Ensures datasets are representative and ethically collected	Reduces bias, ensures fairness and trustworthiness	Bias detection, DEI, ethical data collection	IBM AI Fairness 360, DataSheets, Fairlearn
Robust Model Lifecycle Management (ModelOps)	Manages AI model CI/CD, monitoring, versioning, and rollback	Ensures consistent performance, security, and traceability	DevOps, CI/CD, monitoring, version control	MLflow, Kubeflow, SageMaker Monitor
Continuous Monitoring, Evaluation, and Iterative Improvement	Real-time system and performance evaluation with feedback loops	Catches drift, identifies failures, improves reliability	AIOps, model eval, observability	Prometheus, Grafana, WhyLabs, Arize AI

Closing Thoughts

Testing AI systems requires the same fundamental mindset professional testers have always brought to the quality assurance role: skeptical curiosity, systems thinking, and user advocacy. What’s changed is the complexity of failure modes and the potential stakes involved when things go wrong.

Risk-based heuristics, early involvement (shift-left), cross-functional collaboration, and continuous testing all still apply. But the implementation of those things has to evolve for systems that are probabilistic rather than deterministic, that can fail convincingly rather than obviously, and that exhibit risks we’re collectively still learning to identify.

The good news? QA professionals are uniquely positioned and well equipped to begin handling these challenges. Testers understand user needs, business requirements, and the importance of systematic validation. They just need to apply their existing skills to a new class of problems.

Many of the approaches we’ve discussed above require significant expertise. Some of them require dedicated or specialised tooling. While general testing principles hold, and hopefully inspire you to learn more about how you may apply them within the specific context of your team and organisation, you may be asking “how do I get started from where I am?”

Stay tuned! We’ll cover exactly that in our next article.

References:

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7. Aleti, A. (n.d.). Software Testing of Generative AI Systems: Challenges and Opportunities. [online] Available at: https://arxiv.org/pdf/2309.03554 [Accessed 22 Jul. 2025].
8. arXiv.org e-Print archive . (2024). TRiSM for Agentic AI: A Review of Trust, Risk, and Security Management in LLM-based Agentic Multi-Agent Systems. [online] Available at: TRiSM for Agentic AI: A Review of Trust, Risk, and Security Management in LLM-based Agentic Multi-Agent Systems [Accessed 22 Jul. 2025].