How to Manage Language Model Output Risks

🚨 Public Service Announcement: If you're building LLM-based applications for internal business use, especially for high-risk functions this is for you. Define Context Clearly ------------------------ 📋 Document the purpose, expected behavior, and users of the LLM system. 🚩 Note any undesirable or unacceptable behaviors upfront. Conduct a Risk Assessment ---------------------------- 🔍 Identify potential risks tied to the LLM (e.g., misinformation, bias, toxic outputs, etc), and be as specific as possible 📊 Categorize risks by impact on stakeholders or organizational goals. Implement a Test Suite ------------------------ 🧪 Ensure evaluations include relevant test cases for the expected use. ⚖️ Use benchmarks but complement them with tests tailored to your business needs. Monitor Risk Coverage ----------------------- 📈 Verify that test inputs reflect real-world usage and potential high-risk scenarios. 🚧 Address gaps in test coverage promptly. Test for Robustness --------------------- 🛡 Evaluate performance on varied inputs, ensuring consistent and accurate outputs. 🗣 Incorporate feedback from real users and subject matter experts. Document Everything ---------------------- 📑 Track risk assessments, test methods, thresholds, and results. ✅ Justify metrics and thresholds to enable accountability and traceability. #psa #llm #testingandevaluation #responsibleAI #AIGovernance Patrick Sullivan, Khoa Lam, Bryan Ilg, Jeffery Recker, Borhane Blili-Hamelin, PhD, Dr. Benjamin Lange, Dinah Rabe, Ali Hasan

Did you know what keeps AI systems aligned, ethical, and under control?  The answer: Guardrails Just because an AI model is smart doesn’t mean it’s safe. As AI becomes more integrated into products and workflows, it’s not enough to just focus on outputs. We also need to manage how those outputs are generated, filtered, and evaluated. That’s where AI guardrails come in. Guardrails help in blocking unsafe prompts, protecting personal data and enforcing brand alignment. OpenAI, for example, uses a layered system of guardrails to keep things on track even when users or contexts go off-script. Here’s a breakdown of 7 key types of guardrails powering responsible AI systems today: 1.🔸Relevance Classifier Ensures AI responses stay on-topic and within scope. Helps filter distractions and boosts trust by avoiding irrelevant or misleading content. 2.🔸 Safety Classifier Flags risky inputs like jailbreaks or prompt injections. Prevents malicious behavior and protects the AI from being exploited. 3.🔸 PII Filter Scans outputs for personally identifiable information like names, addresses, or contact details, and masks or replaces them to ensure privacy. 4.🔸 Moderation Detects hate speech, harassment, or toxic behavior in user inputs. Keeps AI interactions respectful, inclusive, and compliant with community standards. 5.🔸 Tool Safeguards Assesses and limits risk for actions triggered by the AI (like sending emails or running tools). Uses ratings and thresholds to pause or escalate. 6.🔸 Rules-Based Protections Blocks known risks using regex, blacklists, filters, and input limits, especially for SQL injections, forbidden commands, or banned terms. 7.🔸 Output Validation Checks outputs for brand safety, integrity, and alignment. Ensures responses match tone, style, and policy before they go live. These invisible layers of control are what make modern AI safe, secure, and enterprise-ready and every AI builder should understand them. #AI #Guardrails

I was interviewed at length for today's The Wall Street Journal article on what exactly went so wrong with Grok. Here's what's critical for any leader considering enterprise-grade AI: Great article by Steve Rosenbush breaking down exactly how AI safety can fail, and why raw capability isn't everything. AI tools need to be trusted by enterprises, by parents, by all of us. Especially as we enter the age of agents, we're looking at tools that won't just answer offensively, they'll take action as well. That's when things really get out of hand. ++++++++++ WHAT WENT WRONG? From the article: "So while the risk isn't unique to Grok, Grok's design choices, real-time access to a chaotic source, combined with reduced internal safeguards, made it much more vulnerable," Grennan said. In other words, this was avoidable. Grok was set up to be "extremely skeptical" and not trust mainstream sources. But when it searched the internet for answers, it couldn't tell the difference between legitimate information and harmful/offensive content like the "MechaHitler" meme. It treated everything it found online as equally trustworthy. This highlights a broader issue: Not all LLMs are created equal, because getting guardrails right is hard. Most leading chatbots (by OpenAI, Google, Microsoft, Anthropic) do NOT have real-time access to social media precisely because of these risks, and they use filtering systems to screen content before the model ever sees it. +++++++++++ WHAT DO LEADERS NEED TO KNOW? 1. Ask about prompt hierarchies in vendor evaluations. Your AI provider should clearly explain how they prioritize different sources of information. System prompts (core safety rules) must override everything else, especially content pulled from the internet. If they can't explain this clearly, that's a red flag. 2. Demand transparency on access controls. Understand exactly what your AI system can read versus what it can actually do. Insist on read-only access for sensitive data and require human approval for any actions that could impact your business operations. 3. Don't outsource responsibility entirely. While you leaders aren't building the AI yourselves, you still own the risk. Establish clear governance around data quality, ongoing monitoring, and incident response. Ask hard questions about training data sources and ongoing safety measures. Most importantly? Get fluent. If you understand how LLMs work, even at a basic level, these incidents will be easier to guard against. Thanks again to Steve Rosenbush for the great article! Link to article in the comments! +++++++++ UPSKILL YOUR ORGANIZATION: When your organization is ready to create an AI-powered culture—not just add tools—AI Mindset can help. We drive behavioral transformation at scale through a powerful new digital course and enterprise partnership. DM me, or check out our website.

A key feature you cannot forget in your GenAI implementation: AI Guardrails 𝗪𝗵𝗮𝘁 𝗮𝗿𝗲 𝗔𝗜 𝗚𝘂𝗮𝗿𝗱𝗿𝗮𝗶𝗹𝘀? Guardrails are programmable rules that act as safety controls between a user and an LLM or other AI tools. 𝗛𝗼𝘄 𝗗𝗼 𝗚𝘂𝗮𝗿𝗱𝗿𝗮𝗶𝗹𝘀 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻 𝘄𝗶𝘁𝗵 𝗔𝗜 𝗠𝗼𝗱𝗲𝗹𝘀? Guardrails monitor communication in both directions and take actions to ensure the AI model operates within an organization's defined principles. 𝗪𝗵𝗮𝘁 𝗶𝘀 𝘁𝗵𝗲 𝗣𝘂𝗿𝗽𝗼𝘀𝗲 𝗼𝗳 𝗜𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁𝗶𝗻𝗴 𝗚𝘂𝗮𝗿𝗱𝗿𝗮𝗶𝗹𝘀 𝗶𝗻 𝗔𝗜 𝗦𝘆𝘀𝘁𝗲𝗺𝘀? The goal is to control the LLM's output, such as its structure, type, and quality, while validating each response. 𝗪𝗵𝗮𝘁 𝗥𝗶𝘀𝗸𝘀 𝗗𝗼 𝗚𝘂𝗮𝗿𝗱𝗿𝗮𝗶𝗹𝘀 𝗠𝗶𝘁𝗶𝗴𝗮𝘁𝗲 𝗶𝗻 𝗔𝗜 𝗦𝘆𝘀𝘁𝗲𝗺𝘀? Guardrails can help prevent AI models from saying incorrect facts, discussing harmful subjects, or opening security holes. 𝗛𝗼𝘄 𝗗𝗼 𝗚𝘂𝗮𝗿𝗱𝗿𝗮𝗶𝗹𝘀 𝗣𝗿𝗼𝘁𝗲𝗰𝘁 𝗔𝗴𝗮𝗶𝗻𝘀𝘁 𝗧𝗲𝗰𝗵𝗻𝗶𝗰𝗮𝗹 𝗧𝗵𝗿𝗲𝗮𝘁𝘀 𝘁𝗼 𝗔𝗜 𝗦𝘆𝘀𝘁𝗲𝗺𝘀? They can protect against common LLM vulnerabilities, such as jailbreaks and prompt injections. Guardrails support three broad categories of guardrails: 1/ Topical guardrails: Ensure conversations stay focused on a particular topic 2/ Safety guardrails: Ensure interactions with an LLM do not result in misinformation, toxic responses, or inappropriate content 3/ Hallucination detection: Ask another LLM to fact-check the first LLM's answer to detect incorrect facts Which guardrails system do you implement in your AI solutions?