Why Testing AI Systems Matters

𝗬𝗼𝘂 𝘄𝗼𝘂𝗹𝗱𝗻’𝘁 𝗱𝗲𝗽𝗹𝗼𝘆 𝗰𝗼𝗱𝗲 𝘄𝗶𝘁𝗵𝗼𝘂𝘁 𝗖𝗜/𝗖𝗗. 𝗦𝗼 𝘄𝗵𝘆 𝗮𝗿𝗲 𝘄𝗲 𝘀𝘁𝗶𝗹𝗹 𝗹𝗮𝘂𝗻𝗰𝗵𝗶𝗻𝗴 𝗔𝗜 𝗺𝗼𝗱𝗲𝗹𝘀 𝘄𝗶𝘁𝗵𝗼𝘂𝘁 𝗰𝗼𝗻𝘁𝗶𝗻𝘂𝗼𝘂𝘀 𝗲𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗼𝗻? A client came to us after shipping their GenAI-powered support bot. Day 1 looked great. Day 7? Chaos. The model had started hallucinating refund policies, mixing up pricing tiers, and answering with outdated terms. None of it showed up during their internal testing. Why? Because they were testing in a bubble. Real users don’t follow your script. They throw curveballs. They type in slang. They copy-paste entire emails into your input box. And eventually... they break your model. That’s why we push for daily, real-world evals. Not just test prompts in a sandbox — but tracking live model behavior in production, flagging weird responses, catching regressions early. Model behavior shifts over time. So should your evaluation. If you wouldn’t ship code without automated tests and monitoring, don’t ship your LLM without it either. Curious — how are you monitoring your model in the wild? Or is it still a black box post-deploy?

I recently reviewed some entry-level AI Agent product training, and something critical was missing: any mention of testing or guardrails. When I flagged this, the feedback was that such topics are reserved for "Level 3 and 4" — where programming comes into play. This line of thinking perfectly illustrates why AI fluency remains stubbornly low, and why we continue to see so many preventable AI incidents. Especially with low-code/no-code tools, where AI can feel like pure magic, there's a dangerous misconception that it "just works." Here's the truth: guardrails and robust testing aren't advanced topics; they are foundational to AI fluency. Think of it this way: Would you build a bridge without testing its structural integrity or installing safety barriers? Of course not. The same common-sense approach applies to AI. Whether you're a seasoned developer or a business user leveraging no-code solutions, understanding how to implement and evaluate safeguards is non-negotiable. Why does this matter for your AI strategy? Your AI strategy isn't just about deploying cool tech; it's about building trust, mitigating risk, and ensuring responsible innovation. When teams lack a fundamental understanding of guardrails and testing from the outset, they are, in essence, operating blind. This can lead to: - Public Incidents: Remember the countless headlines about AI gone awry? Many could have been prevented with proper guardrails and testing. - Erosion of Trust: Each incident erodes public and internal trust in AI's capabilities and your organization's ability to manage it. - Wasted Resources: Fixing problems after deployment is far more costly and time-consuming than building robust systems from the start. - Limited Scalability: Without a clear understanding of limitations and safety nets, scaling AI initiatives becomes a high-risk gamble. AI fluency isn't about being a coder; it's about understanding the technology's capabilities AND its critical limitations. It's about knowing how to ensure your AI behaves ethically, reliably, and safely. If you're building or using AI, no matter the level of abstraction, you must be fluent in the principles of testing and guardrails. It's not a "nice-to-have" for advanced users; it's a "must-have" for everyone involved in AI. And this must be said: this isn't just about avoiding headlines; it's about unlocking the true, positive potential of AI by building it responsibly from the ground up. #AI #AIFluency #Guardrails #AIStrategy #ResponsibleAI

Here’s a real and likely scenario. Your energy bill just spiked. Not because you used more power, but because a company’s AI system mistakenly billed you instead of your local gym, or whoever should have been billed. You call to fix it… and you’re stuck on hold for 47 minutes while another AI “prioritizes your concern.”🗯️ This isn’t just a failure of automation, it’s a failure of governance. The kind that should have been validated before it reached the customer. … Red teaming must evolve, not just for AI systems, but for AI governance. ⚙️ As my colleague reminded me, much of this work has been human-centered, and that’s valuable, but the scale and speed of AI demand more. ⚡ In my last post on this subject, I shared how most testing still focuses on technical exploits. But in the age of AI, the biggest vulnerabilities aren’t always in the code. If we only test the system, but not the structures that govern it, are we really testing the risk? … so here are some follow-up checkpoints (not an exhaustive list) 🔍 1. AI System Red Teaming • Prompt injection, jailbreaks, model drift • Unexpected behavior that appears over time 📊 2. Governance-Aware Red Teaming • Undocumented model swaps • Decision pipelines with unclear accountability And where do AI agents fit in? 3. Human-centered AI agents should: ✅Simulate governance failures ✅Detect untracked model changes ✅Orchestrate 🤔 -Who should own red teaming AI governance in your organization - the security team, the risk office, the board, others? -Are the teams equipped to support innovation at the speed AI demands? If this resonates, feel free to comment or reach out. I welcome thoughtful conversations and I’m here to help. #AI #AIgovernance #RedTeam #Cybersecurity #AIleadership #AIresilience #ResponsibleAI #AIAssurance #Innovation #BoardLeadership

Have you seen GPT-powered Chatbots going wrong? Here's an example and some suggestions. 🚀 Embracing GenAI ChatBots: A Cautionary Tale of Innovation and Responsibility 💡 The Cost of Unchecked AI: Hallucinations in AI, where the system generates false or misleading information, can be more than just a minor hiccup. In the case of Chevrolet, it led to significant reputational damage and customer losses. This highlights a crucial aspect of AI development: the need for strong guardrails. Without them, the consequences can be substantial, both financially and in terms of brand integrity. 🔍The Importance of Internal Testing: Before taking a ChatBot public, it's essential to undergo rigorous internal testing cycles. This isn't just about ironing out technical glitches; it's about ensuring that the AI aligns with your brand's values and customer service standards. Tools like AI Fairness 360, TensorFlow Model Analysis, and LIT (Language Interpretability Tool) can provide valuable insights into your AI's performance and help mitigate risks. 🛠️ Tips for AI Testing: ▶ Diversity in Testing Data: Ensure your training and testing data covers a wide range of scenarios and customer interactions. ▶ Continuous Monitoring: Implement systems for real-time monitoring of AI responses to quickly identify and rectify any inappropriate outputs. ▶ Feedback Loops: Encourage user feedback and integrate it into your AI's learning process to continuously improve its accuracy and relevance. ▶ Internal Testing: Ensure quality testing cycles and internal testing can save the day. 🌐 Conclusion: As we embrace the power of GenAI in ChatBots, let's not forget the lessons learned from instances like Chevrolet's. Implementing AI responsibly means investing in thorough testing and solid guardrails to safeguard against the pitfalls of AI hallucinations. Let's innovate responsibly! How are you testing your AI models? would love to hearing from you. #AIResponsibility #ChatBotInnovation #TechEthics

The Next Big Skill in QA: Testing Custom AI Models and GenAI Apps A massive shift is happening in Quality Assurance—and it’s happening fast. Companies everywhere are hiring QA Engineers who can test custom AI models, GenAI applications, and Agentic AI systems. New tools like: • Promptfoo (benchmarking LLM outputs) • LangTest (robust evaluation of AI models) • And techniques like Red Teaming (stress-testing AI vulnerabilities) are becoming must-haves in the QA toolkit. Why is this important? Traditional QA focused on functionality, UI, and performance. AI QA focuses on: • Hallucination Detection (wrong, fabricated outputs) • Prompt Injection Attacks (hacking through prompts) • Bias, Ethics, and Safety Testing (critical for real-world deployment) ⸻ A few real-world bugs we’re now testing for: • GenAI chatbot refuses service during peak hours due to unexpected token limits. • Agentic AI planner gets stuck in infinite loops when task chaining goes slightly off course. • Custom LLM fine-tuned on internal data leaks confidential information under adversarial prompting. ⸻ New Methodologies Emerging: • Scenario Simulation Testing: Stress-test AI agents in chaotic or adversarial conditions. • Output Robustness Benchmarking: Use tools like Promptfoo to validate quality across models. • Automated Red Teaming Pipelines: Constantly probe AI with bad actors’ mindsets. • Bias & Ethics Regression Suites: Identify when fine-tuning introduces unintended prejudices. ⸻ Prediction: In the next 12-18 months, thousands of new QA roles will be created for AI Quality Engineering. Companies will need specialists who know both AI behavior and software testing fundamentals. The future QA engineer won’t just ask “Does the app work?” They’ll ask: “Is the AI reliable, safe, ethical, and aligned?” Are you ready for the AI QA Revolution? Let’s build the future together. #QA #GenAI #AgenticAI #QualityEngineering #Promptfoo #LangTest #RedTeaming #AIQA

Yesterday, OpenAI shared updates on their efforts to enhance AI safety through red teaming - a structured methodology for testing AI systems to uncover risks and vulnerabilities by combining human expertise with automated approaches. See their blog post: https://lnkd.in/gMvPm5Ew (incl. pic below) OpenAI has been employing red teaming for years, and after initially relying on manual testing by external experts, their approach has evolved to include manual, automated, and mixed methods. Yesterday, they released two key papers: - a white paper on external red teaming practices (see: https://lnkd.in/gcsw6_DG) and - a research study introducing a new automated red teaming methodology (see: https://lnkd.in/gTtTH-QF). ---> 1) Human-Centered Red Teaming includes: - Diverse Team Composition: Red teams are formed based on specific testing goals, incorporating diverse expertise such as natural sciences, cybersecurity, and regional politics. Threat modeling helps prioritize areas for testing, with external experts refining the focus after initial priorities are set by internal teams. - Model Access: Red teamers are provided with model versions aligned to campaign goals. Early-stage testing can identify new risks, while later versions help evaluate planned mitigations. Multiple model versions may be tested during the process. - Guidance and Tools: Clear instructions, appropriate interfaces (e.g., APIs or consumer-facing platforms), and detailed documentation guidelines enable effective testing. These facilitate rapid evaluations, feedback collection, and simulations of real-world interactions. - Data Synthesis: Post-campaign analysis identifies whether examples align with existing policies or necessitate new safeguards. Insights from these assessments inform future automated evaluations and model updates. 2.) Automated Red Teaming: OpenAI has introduced an approach using reinforcement learning to generate diverse and effective testing scenarios. This method scales risk assessment by: - Brainstorming attack strategies (e.g., eliciting unsafe advice). - Training models to identify vulnerabilities through programmatic testing. - Rewarding diversity in simulated attacks to identify gaps beyond common patterns. * * * While OpenAI's methods demonstrate best practices for foundation model providers, businesses deploying AI systems must adopt similar strategies like Bias and Fairness Testing to avoid discrimination, Policy Alignment to uphold ethical standards, and Operational Safety to address risks like unsafe recommendations or data misuse. Without robust testing, issues can arise: customer service agents may give unsafe advice, financial tools might misinterpret queries, and educational chatbots could miss harmful inputs, undermining trust and safety.

I am concerned that there is an over emphasis on procurement as the main mechanism of AI control for the public sector. Government procurement is designed to be scrutable, to promote competition, but not really to support effectiveness. We can buy the wrong thing following the right process. Contracts with terms that are enforceable are helpful, but only to the extent that you verify and enforce them - and really are a distinct part from how we buy things, unless we are using a cooperative purchasing agreement. But really, when we buy things like cloud services we need to rely on audits, on certifications and standards, and the combined power of a lot of buyers that would go somewhere else if the terms of how companies handle their data were not accurately described during the sales and procurement process. Especially with AI and specifically with large models, the computational resources required to run inference are so high that we are likely only buying access to models through a cloud provider that is already in NASPO or a GSA schedule. Virtually anyone can access these services without having to issue an RFP. Evaluation, preferably rigorous evaluation that used edge cases, evaluation with a lense of equity (which really only makes sense at a specific use case), and with human beings that understand the reality behind the language are the only way of really knowing whether a piece of technology meets the needs of a public entity. Testing reveals the reliability and value of what we purchase. Particularly because AI’s jagged frontier line and the rapid changes to the technology (we see 20-30% improvements on the performance of a tool just by using an upgraded version of a model, even without changes to prompts or knowledge), that testing is so important. Instead of seeking sheilds of process, we can have a meaningful impact on our collective understanding of the value of these tools, and push the vendor landscape if we dedicated resources to testing and evaluating. As agents of the Public, government has a role in doing this transparently, and grounded on what real humans that we serve and work with know to be useful or risky.

🚨 Your AI isn't vulnerable - it's turning your existing vulnerabilities into weapons. Traditional security vulnerability + AI capabilities = Catastrophic amplification Here's the real story that happened in one of our AI red teaming engagement: Client built an AI document processor - pretty standard stuff. Upload docs, AI analyzes them, extracts data. They had all the usual security measures: → Input validation → Rate limiting → WAF rules → Access controls But they missed something crucial. A simple SSRF vulnerability (rated "Medium" in traditional apps) became catastrophic when combined with their AI agent because: 1. Chain Reaction: - AI could trigger thousands of internal requests per minute - Each request spawned new processing tasks - Each task inherited system-level privileges 2. Trust Exploitation: - AI service was "trusted" internally - Bypassed traditional security controls - Had direct access to internal services - Could reach restricted networks 3. Privilege Amplification: - What started as a document processor - Became an internal network mapper - Then a data exfiltration pipeline - All using "legitimate" AI functionality The scariest part? This wasn't a sophisticated attack. The AI wasn't "hacked" or "jailbroken." It simply did exactly what it was designed to do - but at a scale and with privileges that turned a simple vulnerability into an enterprise-wide critical risk. 🎯 Key Lesson: Your AI implementations aren't just new features - they're potential amplifiers for every existing vulnerability in your system. Question is: Do you know which of your "moderate" vulnerabilities become critical when your AI capabilities touch them? 👉 Leading AI security testing in 2025 isn't about prompt injection or jailbreak vulnerabilities. It's about understanding how AI agents can transform: - Moderate risks → Critical threats - Local impacts → System-wide breaches - Simple vulnerabilities → Complex attack chains Building AI features? Let's stress test your AI application's security before someone else does. Drop a comment or DM to learn about our AI Red teaming methodology. #AISecurity #AppSec #CyberSecurity #AIRedTeaming #LLMSecurity

What is the importance of "Test, Evaluation, Verification, and Validation" (TEVV) throughout the AI Lifecycle? TEVV tasks are performed throughout the AI lifecycle. (I) Aligning TEVV parameters to AI product requirements can enhance contextual awareness in the AI lifecycle (ii) AI actors who carry out Verification and Validation tasks are distinct from those who perform Test and evaluation actions (iii) TEVV tasks for design, planning, and data may center on internal and external validation of assumptions for system design, data collection, and measurements relative to the intended context of deployment or application. (iv)TEVV tasks for development (i.e., model building) include model validation and assessment. (v)TEVV tasks for deployment include system validation and integration in production, with testing, and recalibration for systems and process integration, user experience, and compliance with existing legal, regulatory, and ethical specifications. (vi) TEVV tasks for operations involve ongoing monitoring for periodic updates, testing, and subject matter expert (SME) recalibration of models, the tracking of incidents or errors reported and their management, the detection of emergent properties and related impacts, and processes for redress and response. Source: NIST AI RMF Figure: NIST AI RMF - Lifecycle and Key Dimensions of an AI System. #ai #artificialintelligence #llm #risklandscape #security #test #evaluation #verification #validation #ailifecycle #nist

Bias doesn’t just creep into AI systems—it cascades. When large language models inherit flawed assumptions or skewed training data, they don’t just replicate vulnerabilities. They amplify them. And worse, they often do so in ways that are hard to detect and even harder to unwind. At HackerOne, we’ve seen this play out in real time. In our AI red teaming engagements, we’ve surfaced everything from prompt injection attacks to logic failures, data leakage, and deeply embedded blind spots that persist across model iterations. These aren’t just technical flaws—they’re reflections of how we build, train, and trust AI systems too quickly and too blindly. Here’s the hard truth: you can’t mitigate what you won’t confront. And bias—whether implicit, inherited, or structural—is a security risk. It's not only about AI safety, trust, or ethics. Testing the security of AI systems isn’t optional. It’s essential. That’s why our approach combines human ingenuity with adversarial testing. We bring in security researchers with diverse perspectives and real-world creativity. People who probe systems in ways automated scanners never could. Because we’ve learned that uncovering AI’s edge cases—its silent failures and unanticipated behaviors—requires more than just compliance checks. It takes a human mind with an attacker’s curiosity and an ally’s intent. The promise of AI is real. But so are the risks. It’s not about fearing the future—it’s about shaping it so that we're not replicating or amplifying the issues of the past. Read more on how we’re confronting AI bias and mitigating its security consequences: https://lnkd.in/g55GjEmb As AI adoption accelerates, how are you testing your models and systems? Who’s challenging the assumptions behind your training data—and how are you bringing human creativity into that loop?