How to Secure Large Language Models

Whether you’re integrating a third-party AI model or deploying your own, adopt these practices to shrink your exposed surfaces to attackers and hackers: • Least-Privilege Agents – Restrict what your chatbot or autonomous agent can see and do. Sensitive actions should require a human click-through. • Clean Data In, Clean Model Out – Source training data from vetted repositories, hash-lock snapshots, and run red-team evaluations before every release. • Treat AI Code Like Stranger Code – Scan, review, and pin dependency hashes for anything an LLM suggests. New packages go in a sandbox first. • Throttle & Watermark – Rate-limit API calls, embed canary strings, and monitor for extraction patterns so rivals can’t clone your model overnight. • Choose Privacy-First Vendors – Look for differential privacy, “machine unlearning,” and clear audit trails—then mask sensitive data before you ever hit Send. Rapid-fire user checklist: verify vendor audits, separate test vs. prod, log every prompt/response, keep SDKs patched, and train your team to spot suspicious prompts. AI security is a shared-responsibility model, just like the cloud. Harden your pipeline, gate your permissions, and give every line of AI-generated output the same scrutiny you’d give a pull request. Your future self (and your CISO) will thank you. 🚀🔐

Yesterday, I laid out the threat of the "Echo Chamber" attack—a stealthy method of turning an LLM's own reasoning against itself to induce a state of localized model collapse. As promised, the deep(er) dive is here. Static defenses can't stop an attack that never trips the alarm. This new class of semantic exploits requires a new class of active, intelligent defense. In this full technical report, I deconstruct the attack vector and detail a multi-layered security strategy that can not only block these threats but learn from them. We'll go beyond simple filters and explore: ► The Semantic Firewall: A system that monitors the state of a conversation to detect the subtle signs of cognitive manipulation. ► The "Turing Interrogator": A reinforcement learning agent that acts as an automated honeypot, actively engaging and profiling attackers to elicit threat intelligence in real time. ► A system diagram illustrating how these components create a resilient, self-improving security ecosystem. The arms race in adversarial AI is here. It's time to build defenses that can think. #AISecurity #LLMSecurity #RedTeaming #CyberSecurity #ModelCollapse #AdversarialAI

AI is not failing because of bad ideas; it’s "failing" at enterprise scale because of two big gaps: 👉 Workforce Preparation 👉 Data Security for AI While I speak globally on both topics in depth, today I want to educate us on what it takes to secure data for AI—because 70–82% of AI projects pause or get cancelled at POC/MVP stage (source: #Gartner, #MIT). Why? One of the biggest reasons is a lack of readiness at the data layer. So let’s make it simple - there are 7 phases to securing data for AI—and each phase has direct business risk if ignored. 🔹 Phase 1: Data Sourcing Security - Validating the origin, ownership, and licensing rights of all ingested data. Why It Matters: You can’t build scalable AI with data you don’t own or can’t trace. 🔹 Phase 2: Data Infrastructure Security - Ensuring data warehouses, lakes, and pipelines that support your AI models are hardened and access-controlled. Why It Matters: Unsecured data environments are easy targets for bad actors making you exposed to data breaches, IP theft, and model poisoning. 🔹 Phase 3: Data In-Transit Security - Protecting data as it moves across internal or external systems, especially between cloud, APIs, and vendors. Why It Matters: Intercepted training data = compromised models. Think of it as shipping cash across town in an armored truck—or on a bicycle—your choice. 🔹 Phase 4: API Security for Foundational Models - Safeguarding the APIs you use to connect with LLMs and third-party GenAI platforms (OpenAI, Anthropic, etc.). Why It Matters: Unmonitored API calls can leak sensitive data into public models or expose internal IP. This isn’t just tech debt. It’s reputational and regulatory risk. 🔹 Phase 5: Foundational Model Protection - Defending your proprietary models and fine-tunes from external inference, theft, or malicious querying. Why It Matters: Prompt injection attacks are real. And your enterprise-trained model? It’s a business asset. You lock your office at night—do the same with your models. 🔹 Phase 6: Incident Response for AI Data Breaches - Having predefined protocols for breaches, hallucinations, or AI-generated harm—who’s notified, who investigates, how damage is mitigated. Why It Matters: AI-related incidents are happening. Legal needs response plans. Cyber needs escalation tiers. 🔹 Phase 7: CI/CD for Models (with Security Hooks) - Continuous integration and delivery pipelines for models, embedded with testing, governance, and version-control protocols. Why It Matter: Shipping models like software means risk comes faster—and so must detection. Governance must be baked into every deployment sprint. Want your AI strategy to succeed past MVP? Focus and lock down the data. #AI #DataSecurity #AILeadership #Cybersecurity #FutureOfWork #ResponsibleAI #SolRashidi #Data #Leadership

𝐈𝐟 𝐲𝐨𝐮 𝐭𝐡𝐢𝐧𝐤 𝐲𝐨𝐮𝐫 𝐋𝐋𝐌 𝐝𝐞𝐩𝐥𝐨𝐲𝐦𝐞𝐧𝐭 𝐢𝐬 𝐬𝐚𝐟𝐞 𝐛𝐞𝐜𝐚𝐮𝐬𝐞 𝐨𝐟 𝐬𝐲𝐬𝐭𝐞𝐦 𝐩𝐫𝐨𝐦𝐩𝐭𝐬 𝐚𝐧𝐝 𝐟𝐢𝐧𝐞-𝐭𝐮𝐧𝐢𝐧𝐠, 𝐭𝐡𝐢𝐧𝐤 𝐚𝐠𝐚𝐢𝐧. HiddenLayer just published a universal bypass that defeats the safety layers of every major model on the market. GPT-4. Claude. Gemini. Llama 3. Copilot. All vulnerable. Same attack pattern. They call it “Policy Puppetry.” It does not need a jailbreak. It manipulates the model into voluntarily ignoring its own rules through clever role prompts and context distortion. This is not a one-off exploit. It is a structural weakness. Because at the end of the day, most “aligned” models are still doing next-token prediction. They are not enforcing rules. They are playing along with patterns. The security lesson is simple. • Alignment is fragile against adversarial prompting • Default safety settings are nowhere near production-grade defense • Real security for AI needs runtime monitoring, adversarial testing, and independent validation pipelines You are not protecting a model. You are protecting a probabilistic system designed to cooperate by default. If you are shipping LLM products without red teaming for these attacks, you are not secure. You are lucky. And luck runs out. Details: https://lnkd.in/g6XkSKEQ

The Secure AI Lifecycle (SAIL) Framework is one of the actionable roadmaps for building trustworthy and secure AI systems. Key highlights include: • Mapping over 70 AI-specific risks across seven phases: Plan, Code, Build, Test, Deploy, Operate, Monitor • Introducing “Shift Up” security to protect AI abstraction layers like agents, prompts, and toolchains • Embedding AI threat modeling, governance alignment, and secure experimentation from day one • Addressing critical risks including prompt injection, model evasion, data poisoning, plugin misuse, and cross-domain prompt attacks • Integrating runtime guardrails, red teaming, sandboxing, and telemetry for continuous protection • Aligning with NIST AI RMF, ISO 42001, OWASP Top 10 for LLMs, and DASF v2.0 • Promoting cross-functional accountability across AppSec, MLOps, LLMOps, Legal, and GRC teams Who should take note: • Security architects deploying foundation models and AI-enhanced apps • MLOps and product teams working with agents, RAG pipelines, and autonomous workflows • CISOs aligning AI risk posture with compliance and regulatory needs • Policymakers and governance leaders setting enterprise-wide AI strategy Noteworthy aspects: • Built-in operational guidance with security embedded across the full AI lifecycle • Lifecycle-aware mitigations for risks like context evictions, prompt leaks, model theft, and abuse detection • Human-in-the-loop checkpoints, sandboxed execution, and audit trails for real-world assurance • Designed for both code and no-code AI platforms with complex dependency stacks Actionable step: Use the SAIL Framework to create a unified AI risk and security model with clear roles, security gates, and monitoring practices across teams. Consideration: Security in the AI era is more than a tech problem. It is an organizational imperative that demands shared responsibility, executive alignment, and continuous vigilance.