AI Guardrails for Responsible Deployment

"we present recommendations for organizations and governments engaged in establishing thresholds for intolerable AI risks. Our key recommendations include: ✔️ Design thresholds with adequate margins of safety to accommodate uncertainties in risk estimation and mitigation. ✔️Evaluate dual-use capabilities and other capability metrics, capability interactions, and model interactions through benchmarks, red team evaluations, and other best practices. ✔️Identify “minimal” and “substantial” increases in risk by comparing to appropriate base cases. ✔️Quantify the impact and likelihood of risks by identifying the types of harms and modeling the severity of their impacts. ✔️Supplement risk estimation exercises with qualitative approaches to impact assessment. ✔️Calibrate uncertainties and identify intolerable levels of risk by mapping the likelihood of intolerable outcomes to the potential levels of severity. ✔️Establish thresholds through multi-stakeholder deliberations and incentivize compliance through an affirmative safety approach. Through three case studies, we elaborate on operationalizing thresholds for some intolerable risks: ⚠️ Chemical, biological, radiological, and nuclear (CBRN) weapons, ⚠️ Evaluation Deception, and ⚠️ Misinformation. " Nada Madkour, PhD Deepika Raman, Evan R. Murphy, Krystal Jackson, Jessica Newman at the UC Berkeley Center for Long-Term Cybersecurity

The proliferation of AI agents, particularly the rise of "shadow autonomy" presents a fundamental security challenge to the industry. While comprehensive controls for Agentic AI identities, Agentic AI applications, MCP, and RAG are discussed in the previous blogs, the core issue lies in determining the appropriate level of security for each agent type, rather than implementing every possible control everywhere. This is not a matter of convenience, but a critical security imperative. The foundational principle for a resilient AI system is to rigorously select a pattern that is commensurate with the agent’s complexity and the potential risk it introduces. These five patterns are the most widely used in agentic AI use cases, and identifying the right patterns or anti-patterns and controls is critical to adopting AI with necessary governance and security. 🟥 UNATTENDED SYSTEM AGENTS How It Works: Run without user consent, authenticated by system tokens. Risk: HIGH Use Cases: Background AI data processing, monitoring, data annotation, and event classification. Controls: ✅ Trusted event sources ✅ Read-only or data enrichment actions ✅ MTLS for strong auth ✅ Prompt injection guardrails Anti-Patterns: ❌ Access to untrusted inputs ❌ Arbitrary code/external calls 🟥 USER IMPERSONATION AGENTS How It Works: Act as a proxy with the user’s token (OAuth/JWT). Risk: HIGH Use Cases: Assistants retrieving knowledge, dashboards, low-risk workflows. Controls: ✅ Read-only or limited APIs ✅ Output guardrails ✅ MTLS Anti-Patterns: ❌ Write/state-changing ops ❌ Privileged APIs 🟨 ATTENDED SYSTEM AGENTS How It Works: Service identity with OAuth/API tokens, with human approval required. Risk: MEDIUM Use Cases: DevSec AI, privileged updates, infra changes. Controls: ✅ Explicit user approval ✅ Logging & audits ✅ MTLS Anti-Patterns: ❌ Blanket downstream access ❌ Unsafe ops (delete/shutdown) ❌ Unmanaged API escalation 🟩 USER DELEGATED AGENTS How It Works: OAuth 2.0 on-behalf-of token (OBO) exchange binds user + agent with consent and traceability. Risk: LOW Use Cases: Recommended for high-risk agent autonomy Controls: ✅ Time-bound consent ✅ Strict API scoping ✅ MTLS Anti-Patterns: ❌ Long-lived refresh tokens ❌ Write/state-changing ops 🟥 MULTI-AGENT SYSTEMS (MAS) How It Works: Multiple agents coordinate with dynamic identities. Hybrid + third-party. Risk: HIGH Use Cases: Decentralized AI with hybrid, in-house + vendor agents. Controls: ✅ Federated SSO ✅ MTLS for all comms ✅ Dynamic authorization ✅ Behavior monitoring ✅ MAS incident response Anti-Patterns: ❌ Static tokens ❌ No custody chain ❌ No secure framework ⚖️ BOTTOM LINE: Security controls must map to agent complexity and risk. From high-risk impersonation to low-risk delegated models with explicit consent and traceability, these patterns deliver proportionate controls, governance, and resilience in agentic AI adoption. #AgenticAI #AISecurity #ShadowAutonomy

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.

AI in healthcare won’t scale without guardrails. The JuLIA Handbook just delivered the clearest blueprint I’ve seen this year. What stood out most: the EU frames most healthcare AI as high-risk, anchored in the rights to health, privacy, and non-discrimination. In plain terms: if your model informs clinical decisions, it must be treated like a safety-critical system, explainable, logged, monitored. The good news? The principles are practical. WHO’s 6 ethical anchors translate cleanly into implementation steps: ✅Autonomy → Keep a human in the loop ✅Safety → Validate before go-live ✅Transparency → Write plain-English docs ✅Equity → Run subgroup testing ✅Accountability → Assign ownership and auditability ✅Sustainability → Plan for lifecycle updates Small clinics and vendors can start simple: ❌Map purpose and context – Where does AI assist care? Who can override it? ❌Prove data quality – Document sources, representativeness, and update policy. ❌Monitor performance – Track by subgroup and set alert thresholds. ❌Log everything – Keep decision logs and incident reports regulators can read. ❌Align consent and info – Tell patients what the model does, in language they understand. The payoff: safer decisions, fewer surprises, faster audits, and long-term trust. If you’re leading a clinic or building tools for one, Which of these 5 steps would you tackle first?

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

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?

We can build AI that amplifies human potential without compromising safety. The key lies in defining clear red lines. When AI systems were simple tools, reactive safety worked. As they gain autonomy and capability, we need clear boundaries on what these tools can and should help humans accomplish - not to limit innovation, but to direct them toward human benefit. Our Global Future Council on the Future of #AI at the World Economic Forum just published findings on "behavioral red lines" for AI. Think of them as guardrails that prevent harm without blocking progress. What makes an effective red line? Read more here: https://lnkd.in/g-x7Sb73 Clarity: The boundary must be precisely defined and measurable Unquestionable: Violations must clearly constitute severe harm Universal: Rules must apply consistently across contexts and borders These qualities matter. Without them, guardrails become either unenforceable or meaningless. Together, we identified critical red lines in our daily tech tools such as systems that self-replicate without authorization, hack other systems, impersonate humans, or facilitate dangerous weapons development. Each represents a point where AI's benefits are overshadowed by potential harm. Would we build nuclear facilities without containment systems? Of course not. Why then do we deploy increasingly powerful AI without similar safeguards? Enforcement requires both prevention and accountability. We need certification before deployment, continuous monitoring during operation, and meaningful consequences for violations. This work reflects the thinking of our Global Future Council, including Pascale Fung, Adrian Weller, Constanza Gomez Mont, Edson Prestes, Mohan Kankanhalli, Jibu Elias, Karim Beguir, and Stuart Russell, with valuable support from the WEF team, including Benjamin Cedric Larsen, PhD. I'm also attaching here our White Paper on AI Value Alignment - where our work was led by the brilliant Virginia Dignum. #AIGovernance #AIEthics #TechPolicy #WEF #AI #Ethics #ResponsibleAI #AIRegulation The Patrick J. McGovern Foundation Satwik Mishra Anissa Arakal

A New Path for Agile AI Governance To avoid the rigid pitfalls of past IT Enterprise Architecture governance, AI governance must be built for speed and business alignment. These principles create a framework that enables, rather than hinders, transformation: 1. Federated & Flexible Model: Replace central bottlenecks with a federated model. A small central team defines high-level principles, while business units handle implementation. This empowers teams closest to the data, ensuring both agility and accountability. 2. Embedded Governance: Integrate controls directly into the AI development lifecycle. This "governance-by-design" approach uses automated tools and clear guidelines for ethics and bias from the project's start, shifting from a final roadblock to a continuous process. 3. Risk-Based & Adaptive Approach: Tailor governance to the application's risk level. High-risk AI systems receive rigorous review, while low-risk applications are streamlined. This framework must be adaptive, evolving with new AI technologies and regulations. 4. Proactive Security Guardrails: Go beyond traditional security by implementing specific guardrails for unique AI vulnerabilities like model poisoning, data extraction attacks, and adversarial inputs. This involves securing the entire AI/ML pipeline—from data ingestion and training environments to deployment and continuous monitoring for anomalous behavior. 5. Collaborative Culture: Break down silos with cross-functional teams from legal, data science, engineering, and business units. AI ethics boards and continuous education foster shared ownership and responsible practices. 6. Focus on Business Value: Measure success by business outcomes, not just technical compliance. Demonstrating how good governance improves revenue, efficiency, and customer satisfaction is crucial for securing executive support. The Way Forward: Balancing Control & Innovation Effective AI governance balances robust control with rapid innovation. By learning from the past, enterprises can design a resilient framework with the right guardrails, empowering teams to harness AI's full potential and keep pace with business. How does your Enterprise handle AI governance?

Relying on one LLM provider like OpenAI is risky and often leads to unnecessary high costs and latency. But there's another critical challenge: ensuring LLM outputs align with specific guidelines and safety standards. What if you could address both issues with a single solution? This is the core promise behind Portkey's open-source AI Gateway. AI Gateway is an open-source package that seamlessly integrates with 200+ LLMs, including OpenAI, Google Gemini, Ollama, Mistral, and more. It not only solves the provider dependency problem but now also tackles the crucial need for effective guardrails by partnering with providers such as Patronus AI and Aporia. Key features: (1) Effortless load balancing across models and providers (2) Integrated guardrails for precise control over LLM behavior (3) Resilient fallbacks and automatic retries to guarantee your application recovers from failed LLM API requests (4) Adds minimal latency as a middleware (~10ms) (5) Supported SDKs include Python, Node.JS, Rust, and more One of the main hurdles to enterprise AI adoption is ensuring LLM inputs and outputs are safe and adhere to your company’s policies. This is why projects like Portkey are so useful. Integrating guardrails into an AI gateway creates a powerful combination that orchestrates LLM requests based on predefined guardrails, providing precise control over LLM outputs. Switching to more affordable yet performant models is a useful technique to reduce cost and latency for your app. I covered this and eleven more techniques in my last AI Tidbits Deep Dive https://lnkd.in/gucUZzYn GitHub repo https://lnkd.in/g8pjgT9R

International collaboration on AI risk management standards is critical if we want organizations to have tools that are effective, efficient and safe. I want to applaud the U.S. Department of Commerce and National Institute of Standards and Technology (NIST) for its latest updates to the NIST AI Risk Management Framework released on Friday. https://lnkd.in/ghAghiYA The focus on reducing threats to training data (like poisoning LLMs) is critical and something Moveworks spends a lot of time evaluating each model we use. We’re glad that this is moving to be the standard in the US. The creation of Global Engagement on AI Standards will hopefully lead to more collaboration on AI standards, something critical for global enterprises like Moveworks. Since our inception, Moveworks has focused on secure, private, and responsible AI development. https://lnkd.in/g8Ercsyt We’ve invested tremendous engineering resources to create guardrails and controls from day one, putting our copilot through rigorous testing to ensure our customers get access to the safest and most secure enterprise copilot in the world. That is why it was encouraging when last year NIST released an AI risk management framework that mirrors our mindset to secure AI development. Their framework of a continuous improvement model predicated on governance, mapping, measuring, and managing AI risks speaks directly to how we think about mitigating risks from LLMs. Through this type of framework, Moveworks has built an enterprise copilot, used by millions, that has safeguards around hallucinations, is protected against prompt injections, and ensures we’ve developed plugins that respect access controls and permissions.