Building Guardrails with AI Language Models

Shipping AI agents into production without governance is like deploying software without security, logs, or controls. It might work at first. But sooner or later, something breaks - silently. As AI agents move from experiments to real decision-makers, governance becomes infrastructure. This framework breaks AI Governance into the core functions every production-grade agent system needs: - Policy Rules Turn business and regulatory expectations into enforceable agent behavior - defining what agents can do, must avoid, and how they respond in restricted scenarios. - Access Control Limits agents to approved tools, datasets, and systems using identity verification, RBAC, and permission boundaries — preventing accidental or malicious misuse. - Audit Logs Create a full activity trail of agent decisions: what data was accessed, which tools were called, and why actions were taken — making every outcome traceable. - Risk Scoring Evaluates agent actions before execution, assigns risk levels, detects sensitive operations, and blocks unsafe decisions through thresholds and safety scoring. - Data Privacy Protects confidential information using PII detection, encryption, consent management, and retention policies — ensuring agents don’t leak regulated data. - Model Monitoring Tracks real-world agent performance: accuracy, drift, hallucinations, latency, and cost - keeping systems reliable after deployment. - Human Approvals Adds human-in-the-loop controls for high-impact actions, enabling escalation, overrides, and sign-offs when automation alone isn’t enough. - Incident Response Detects failures early and enables rapid containment through alerts, rollbacks, kill switches, and post-incident reporting to prevent repeat issues. The takeaway: AI agents don’t just need intelligence. They need guardrails. Without governance, agents become unpredictable. With governance, they become enterprise-ready. This is how organizations move from experimental AI to trustworthy, compliant, production systems. Save this if you’re building agentic systems. Share it with your platform or ML teams.

In my work with organizations rolling out AI and generative AI solutions, one concern I hear repeatedly from leaders, and the c-suite is how to get a clear, centralized “AI Risk Center” to track AI safety, large language model's accuracy, citation, attribution, performance and compliance etc. Operational leaders want automated governance reports—model cards, impact assessments, dashboards—so they can maintain trust with boards, customers, and regulators. Business stakeholders also need an operational risk view: one place to see AI risk and value across all units, so they know where to prioritize governance. One of such framework is MITRE’s ATLAS (Adversarial Threat Landscape for Artificial-Intelligence Systems) Matrix. This framework extends MITRE ATT&CK principles to AI, Generative AI, and machine learning, giving us a structured way to identify, monitor, and mitigate threats specific to large language models. ATLAS addresses a range of vulnerabilities—prompt injection, data leakage, malicious code generation, and more—by mapping them to proven defensive techniques. It’s part of the broader AI safety ecosystem we rely on for robust risk management. On a practical level, I recommend pairing the ATLAS approach with comprehensive guardrails - such as: • AI Firewall & LLM Scanner to block jailbreak attempts, moderate content, and detect data leaks (optionally integrating with security posture management systems). • RAG Security for retrieval-augmented generation, ensuring knowledge bases are isolated and validated before LLM interaction. • Advanced Detection Methods—Statistical Outlier Detection, Consistency Checks, and Entity Verification—to catch data poisoning attacks early. • Align Scores to grade hallucinations and keep the model within acceptable bounds. • Agent Framework Hardening so that AI agents operate within clearly defined permissions. Given the rapid arrival of AI-focused legislation—like the EU AI Act, now defunct  Executive Order 14110 of October 30, 2023 (Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence) AI Act, and global standards (e.g., ISO/IEC 42001)—we face a “policy soup” that demands transparent, auditable processes. My biggest takeaway from the 2024 Credo AI Summit was that responsible AI governance isn’t just about technical controls: it’s about aligning with rapidly evolving global regulations and industry best practices to demonstrate “what good looks like.” Call to Action: For leaders implementing AI and generative AI solutions, start by mapping your AI workflows against MITRE’s ATLAS Matrix. Mapping the progression of the attack kill chain from left to right - combine that insight with strong guardrails, real-time scanning, and automated reporting to stay ahead of attacks, comply with emerging standards, and build trust across your organization. It’s a practical, proven way to secure your entire GenAI ecosystem—and a critical investment for any enterprise embracing AI.

Few-shot Text Classification predicts the label of a given text after training with just a handful of labeled data. It's a powerful technique for overcoming real-world situations with scarce labeled data. SetFit is a fast, accurate few-shot NLP classification model perfect for intent detection in GenAI chatbots. In the pre-ChatGPT era, Intent Detection was an essential aspect of chatbots like Dialogflow. Chatbots would only respond to intents or topics that the developers explicitly programmed, ensuring they would stick closely to their intended use and prevent prompt injections. OpenAI's ChatGPT changed that with its incredible reasoning abilities, which allowed an LLM to decide how to answer users' questions on various topics without explicitly programming a flow for handling each topic. You just "prompt" the LLM on which topics to respond to and which to decline and let the LLM decide. However, numerous examples in the post-ChatGPT era have repeatedly shown how finicky a pure "prompt" based approach is. In my journey working with LLMs over the past year+, one of the most reliable methods I've found to restrict LLMs to a desired domain is to follow a 2-step approach that I've spoken about in the past: https://lnkd.in/g6cvAW-T 1. Preprocessing guardrail: An LLM call and heuristical rules to decide if the user's input is from an allowed topic. 2. LLM call: The chatbot logic, such as Retrieval Augmented Generation. The downside of this approach is the significant latency added by the additional LLM call in step 1. The solution is simple: replace the LLM call with a lightweight model that detects if the user's input is from an allowed topic. In other words, good old Intent Detection! With SetFit, you can build a highly accurate multi-label text classifier with as few as 10-15 examples per topic, making it an excellent choice for label-scarce intent detection problems. Following the documentation from the links below, I could train a SetFit model in seconds and have an inference time of <50ms on the CPU! If you're using an LLM as a few- or zero-shot classifier, I recommend checking out SetFit instead! 📝 SetFit Paper: https://lnkd.in/gy88XD3b 🌟 SetFit Github: https://lnkd.in/gC8br-EJ 🤗 SetFit Few Shot Learning Blog on Huggingface: https://lnkd.in/gaab_tvJ 🤗 SetFit Multi-Label Classification: https://lnkd.in/gz9mw4ey 🗣️ Intents in DialogFlow: https://lnkd.in/ggNbzxH6 Follow me for more tips on building successful ML and LLM products! Medium: https://lnkd.in/g2jAJn5 X: https://lnkd.in/g_JbKEkM #generativeai #llm #nlp #artificialintelligence #mlops #llmops

One of today’s most pressing AI challenges is the gap between a Large Language Model’s raw intelligence and how that intelligence translates into consistent, real-world performance when powering autonomous AI agents. This challenge is known as “jagged intelligence.” To strengthen customer trust and tackle jaggedness head-on, Salesforce AI Research is applying rigorous benchmarking, continuous testing, and robust guardrails. By systematically evaluating agent behaviour against real-world conditions and setting clear boundaries for performance and safety, Salesforce #AI Research is also ensuring agents behave consistently, predictably, and reliably in enterprise environments. Here’s how we’re engineering #trust into every agent: ➊ A new framework designed to test and evaluate AI agents: Evaluating enterprise AI agents’ ability to perform business-level tasks is a critical priority and a persistent challenge for CIOs and IT leaders. ➋ New agent guardrail features enhance trust and security: Agentforce’s guardrails establish clear boundaries for agent behaviour based on business needs, policies, and standards, ensuring agents act within predefined limits. ➌ A new benchmark for assessing models in contextual settings: Ensuring AI generates accurate, contextual answers is crucial for enterprise trust, but traditional benchmarks often fall short. Explore our innovative research: https://lnkd.in/eT_MdG_b

Agents aren’t magic. They’re models, tools, and instructions stitched together—with the right guardrails. 🤖 What’s an agent? Systems that independently accomplish tasks on your behalf—recognize completion, choose tools, recover from failure, and hand control back when needed. 🧰 Agent foundations (the big 3): Model for reasoning, Tools for action/data, and Instructions for behavior/guardrails. Keep them explicit and composable. 🧠 When to build an agent (not just automation): Use cases with nuanced judgment, brittle rules, or heavy unstructured data—think refunds, vendor reviews, or claims processing. 🧪 Model strategy that actually works: Prototype with the most capable model to set a baseline → evaluate → swap in smaller models where accuracy holds to cut cost/latency. 🛠️ Tooling patterns: Standardize tool definitions; separate Data, Action, and Orchestration tools; reuse across agents to avoid prompt bloat. 🧩 Orchestration choices: Start with a single agent + looped “run” until exit. Scale to multi-agent when logic branches/overlapping tools get messy (Manager vs. Decentralized handoffs). 📝 Instruction design tips: Break tasks into steps, map each step to a concrete action/output, capture edge cases, and use prompt templates with policy variables. 🛡️ Guardrails = layered defense: Combine relevance/safety classifiers, PII filters, moderation, regex/rules, tool-risk ratings, and output validation—plus human-in-the-loop for high-risk actions. 🧭 Pragmatic rollout mindset: Ship small, learn from real users, add guardrails as you discover edge cases, and iterate toward reliability. #AI #Agents #AgenticAI #GenAI #LLM #AIProduct #MLOps #PromptEngineering #AIGuardrails #Automation

Conversational AI is transforming customer support, but making it reliable and scalable is a complex challenge. In a recent tech blog, Airbnb’s engineering team shares how they upgraded their Automation Platform to enhance the effectiveness of virtual agents while ensuring easier maintenance. The new Automation Platform V2 leverages the power of large language models (LLMs). However, recognizing the unpredictability of LLM outputs, the team designed the platform to harness LLMs in a more controlled manner. They focused on three key areas to achieve this: LLM workflows, context management, and guardrails. The first area, LLM workflows, ensures that AI-powered agents follow structured reasoning processes. Airbnb incorporates Chain of Thought, an AI agent framework that enables LLMs to reason through problems step by step. By embedding this structured approach into workflows, the system determines which tools to use and in what order, allowing the LLM to function as a reasoning engine within a managed execution environment. The second area, context management, ensures that the LLM has access to all relevant information needed to make informed decisions. To generate accurate and helpful responses, the system supplies the LLM with critical contextual details—such as past interactions, the customer’s inquiry intent, current trip information, and more. Finally, the guardrails framework acts as a safeguard, monitoring LLM interactions to ensure responses are helpful, relevant, and ethical. This framework is designed to prevent hallucinations, mitigate security risks like jailbreaks, and maintain response quality—ultimately improving trust and reliability in AI-driven support. By rethinking how automation is built and managed, Airbnb has created a more scalable and predictable Conversational AI system. Their approach highlights an important takeaway for companies integrating AI into customer support: AI performs best in a hybrid model—where structured frameworks guide and complement its capabilities. #MachineLearning #DataScience #LLM #Chatbots #AI #Automation #SnacksWeeklyonDataScience – – –  Check out the "Snacks Weekly on Data Science" podcast and subscribe, where I explain in more detail the concepts discussed in this and future posts:    -- Spotify: https://lnkd.in/gKgaMvbh   -- Apple Podcast: https://lnkd.in/gj6aPBBY    -- Youtube: https://lnkd.in/gcwPeBmR https://lnkd.in/gFjXBrPe

When should you let AI run free, and when do you need a human in the loop? As PMs build AI into products, we can't treat every output the same way. A restaurant recommendation gone wrong is annoying. A food allergy mistake could be deadly. In the latest Product Thinking podcast episode, we combine the insights of three amazing guests and Maryam Ashoori, PhD's take on guardrails was really insightful! The framework is simple: identify what's non-negotiable in your product, then build guardrails around those moments. When stakes are low, let the AI move fast. When stakes are high, add validation layers or require human review. Take that restaurant example. "Where should I eat Italian tonight?" can run with minimal checks. But the second someone mentions peanut allergies, the entire risk profile changes. You need different safeguards for different scenarios. This means rethinking how you design AI workflows. Map out where errors could cause real harm. Those are your checkpoints. Everything else can flow faster. The goal isn't to slow down innovation with endless validation. It's to be intentional about where speed matters and where safety can't be compromised.

🔐 The A.G.E.N.T. Security Framework: A practical model for securing Agentic AI systems at enterprise scale. 🚨 Lots of orgs are flying blind into Agentic AI. Without a maturity model, chaos is inevitable. 👀 Introducing A.G.E.N.T. Security Framework 👇🏾 The A.G.E.N.T. Security Framework is a 5-phase guide I’ve build with other CISOs & Security Leaders which helped them move from AI chaos → clarity. The 5 Phases of A.G.E.N.T. 🕵🏾♀️ Awareness (A) Shadow AI adoption creates blind spots. Guardrails: governance councils, discovery tools, acceptable use. 🛡️ Governance (G) Copilots enter workflows; identity sprawl + data leakage risk explode. Guardrails: structured onboarding, scoped access, policy consistency. 🏗️ Engineering (E) Enterprises build “paved roads” with MCP servers, LLMs, APIs. Guardrails: sandbox testing, lifecycle governance, token management, version control. 🧭 Navigation (N) Semi-agentic AI starts acting in production. Guardrails: runtime policies, rollback paths, anomaly detection. 💪🏾 Trust (T) Even 95% accurate agents can cascade failures. Guardrails: human-in-loop for high-impact moves, escalation workflows, dashboards. 📊 For CISOs & Tech Leaders: ✔️ Map your org against these 5 phases ✔️ Identify missing guardrails ✔️ Decide where to invest 𝘯𝘦𝘹𝘵 𝘲𝘶𝘢𝘳𝘵𝘦𝘳 💡 Lesson: Without guardrails, small cracks become systemic risks. With them, AI can scale securely without killing innovation. 👉🏾  Most orgs stall between Awareness & Governance. 👀 Want the full A.G.E.N.T. maturity playbook with risks + guardrails mapped? Comment “AGENT” and I’ll share it with you. Question for you: Which phase feels most real in your org today? (see infographics below) 👇🏾 ---------------------------------------------------------------------- 🎙️ I’ll unpack this on the 𝗖𝗹𝗼𝘂𝗱 𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆 𝗣𝗼𝗱𝗰𝗮𝘀𝘁 & 𝗔𝗜 𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆 𝗣𝗼𝗱𝗰𝗮𝘀𝘁 next week -  available on Apple, Spotify, YouTube, LinkedIn. You can now Save 🔖 this post to revisit and come back later when you need to revisit in a easy place to find. 😎 If you're looking to keep up on latest AI strategy, security, and scalability: 🔹 Follow Ashish Rajan for insights tailored to CISOs & Security Practitioners ♻️ Repost to help others to cut through the noise around AI Security. #𝗔𝗜𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆 #AI #Cybersecurity

Why 90% of Candidates Fail RAG (Retrieval-Augmented Generation) Interviews You know how to call the OpenAI API. You’ve built a chatbot using LangChain. You’ve even added a vector database like Pinecone or FAISS. But then the interview happens: • Design a multilingual enterprise RAG pipeline • Optimize retrieval latency for 100M documents • Implement query understanding with hybrid search • Build guardrails for hallucination control in production Sound familiar? Most candidates freeze because they’ve only built “toy RAG demos”—never thought about enterprise-scale RAG systems. ⸻ The gap isn’t retrieval—it’s end-to-end RAG system design. Here’s what top candidates do differently: • Instead of: I’ll just embed documents and query them They ask: How do I chunk documents optimally, avoid semantic drift, and handle multilingual embeddings? • Instead of: I’ll just store vectors in Pinecone They ask: How do I design tiered storage (hot vs. cold), caching, and hybrid retrieval (BM25 + dense) to balance speed and accuracy? • Instead of: I’ll let the LLM generate answers They ask: How do I add rerankers, context window optimizers, and confidence scoring to minimize hallucinations? • Instead of: I’ll just call GPT-4 They ask: How do I implement cost-aware routing (open-source models first, GPT fallback) with prompt optimization? ⸻ Why senior AI engineers stand out They don’t just connect an LLM to a database—they design scalable, resilient, and explainable RAG ecosystems. They think about: • Retrieval accuracy vs. latency trade-offs • Vector DB sharding and replication strategies • Monitoring retrieval quality & query drift • Governance: logging, traceability, and compliance That’s why they clear FAANG and top AI company interviews. ⸻ My practice scenarios To prepare, I’ve been tackling real RAG system design challenges like: 1. Designing a multilingual enterprise RAG pipeline with cross-lingual embeddings. 2. Building a retrieval layer with hybrid search + rerankers for better precision. 3. Designing a caching and cost-optimization strategy for high-traffic RAG systems. 4. Implementing guardrails with policy-based filtering and hallucination detection. 5. Architecting RAG pipelines with orchestration tools like LangGraph or n8n. 👉 Most fail because they focus on the model, not the retrieval architecture + system design. Those who succeed show they can build ChatGPT-like RAG systems at scale. If you found this helpful, please like & share—it’ll help others prepping for RAG interviews too.

When you hear the phrase 'data distribution', your first instinct might be to tune out. But if you’re serious about shaping your organisations AI strategy, it’s one of the most important ideas to comprehend. Large language models like ChatGPT and Gemini learn by consuming oceans of text and images. Hidden within those vast datasets are patterns - the world’s concepts, relationships, and associations. The training data distribution describes how all those examples are spread out and connected. Inside that space, models perform astonishingly well: they compress knowledge into abstract patterns that let them talk confidently about everything from quantum mechanics to cooking recipes. But the edges of that distribution are treacherous. Feed a model something truly unfamiliar - something it’s never seen before - and performance doesn’t just decline, it collapses. It’s almost like triggering an adversarial attack: what was superhuman suddenly becomes sub–five-year-old. The model simply doesn’t know what it doesn’t know. That’s the first key insight. Models can still make dangerous mistakes in your area of expertise. The second insight is equally striking: everything inside the general training distribution has become cheap. Tasks and information that live within it - public facts, translation, summarisation, boilerplate code - are now commodities. Competing there is a race to zero. So the strategic question becomes: what does your organisation know that lies outside of the general distribution? What unique knowledge, experience, or data sits beyond what the models already contain? This realisation splits the playbook in two. On one side lies context, guardrails and resilience - internal systems that provide the missing context and recognise when a model has stepped out of its distribution. Think detection, retrieval grounding, context engineering, and oversight. These keep your agents safe and dependable. On the other side lies distinctiveness and value - identifying, structuring, and protecting the knowledge that only you possess. Every organisation has it: proprietary methods, tacit expertise, or specialised data. The challenge is organising it, connecting it together and then protecting it. That’s where knowledge graphs and ontologies step in. Ontologies capture meaning with mathematical precision; knowledge graphs connect that meaning to data. When integrated with your AI, they provide the guardrails that make models safer - and the distinctive context that makes them more accurate and valuable. The shared knowledge of humanity is being commoditised. What remains valuable is the uncommon - the structured, explainable, defensible edge of understanding that only you own. That’s why understanding data distribution matters. You need to know where the general distribution ends - so you can see where your advantage begins.