Best Practices for Securing LLMs in High-Stakes Workflows

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. 🚀🔐

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.

LLM Grammars - That one thing they don't tell you... This is the tech I don't see a lot of folks talking about - and they should be. Since I've introduced agentic behavior and tool exposure in previous posts, I can now provide a good way to make LLMs and their frameworks more secure. To secure your agentic LLMs, LLM tool use, or even LLMs as a logic widget, you absolutely *MUST* use grammars if they are available on your AI inference platform. Grammars, using a modified BNF (Backus–Naur form) notation, are something I've only recently re-discovered since they've been implemented in one of the most common LLM related libraries, GGML, which underpins llama.cpp. If you are developer, you likely got cozy with BNF in your compiler theory class. This allows me to specify a BNF-like template that forces output token compliance. Instead of giving the LLM a set of instructions and hoping that it complies to the output format, I can have the inference server reject tokens that don't match. This rejection happens as they are generated and then continues generating until it outputs what I've requested or it times out. Imagine if you needed a simple "true" or "false" answer from a model. Here's a scenario. -Prompt- Answer the question with a single word, "true" or "false". true or false: All eggs come from birds. -Response- It is true that all birds lay eggs but all eggs do not come from birds, so the statement is false. Other animals like reptiles and fish often lay eggs too. The model has completely ignored the instructions. Now, you try to parse the "true" or "false" out of the natural language. The next time the model answers, it may generate something else in a different orientation. To solve this, I pass in a simple grammar. ``` root ::= "true" | "false" ``` This grammar forces the only tokens to be accepted to be either true or false. The model will eventually land on the appropriate answer - or the model will time out the request. This prevents headaches in parsing later and can ultimately make processing data safer. This grammar is simple and the scenario is overly simplified. You can make a grammar for a JSON response, pre-specified "function calling", or just about anything else you could want to restrict an LLM from producing. How is this different from just filtering output? Well, the answer is nuanced, because it is filtering output but at the token level. It also forces the model to regenerate the token until it meets the BNF filter's specification. This preserves contextual meanings and prevents chopped up and broken syntax when more complex situations arise. #llmsecurity #llm_grammars #bnf #ggml_grammars image: Open AI DALL-E's impression of a "grammar tree"

SAP Customer Data security when using 3rd party LLM's SAP ensures the security of customer data when using third-party large language models (LLMs) through a combination of robust technical measures, strict data privacy policies, and adherence to ethical guidelines. Here are the key strategies SAP employs: 1️⃣ Data Anonymization ↳ SAP uses data anonymization techniques to protect sensitive information. ↳ The CAP LLM Plugin, for example, leverages SAP HANA Cloud's anonymization capabilities to remove or alter personally identifiable information (PII) from datasets before they are processed by LLMs. ↳ This ensures that individual privacy is maintained while preserving the business context of the data. 2️⃣ No Sharing of Data with Third-Party LLM Providers ↳ SAP's AI ethics policy explicitly states that they do not share customer data with third-party LLM providers for the purpose of training their models. ↳ This ensures that customer data remains secure and confidential within SAP's ecosystem. 3️⃣ Technical and Organizational Measures (TOMs) ↳ SAP constantly improves upon its Technical and Organizational Measures (TOMs) to protect customer data against unauthorized access, changes, or deletions. ↳ These measures include encryption, access controls, and regular security audits to ensure compliance with global data protection laws. 4️⃣ Compliance with Global Data Protection Laws ↳ SAP adheres to various global data protection regulations, such as GDPR, CCPA, and others. ↳ They have implemented a Data Protection Management System (DPMS) to ensure compliance with these laws and to protect the fundamental rights of individuals whose data is processed by SAP. 5️⃣ Ethical AI Development ↳ SAP's AI ethics policy emphasizes the importance of data protection and privacy. They follow the 10 guiding principles of the UNESCO ↳ Recommendation on the Ethics of Artificial Intelligence, which include privacy, human oversight, and transparency. ↳ This ethical framework governs the development and deployment of AI solutions, ensuring that customer data is handled responsibly. 6️⃣ Security Governance and Risk Management ↳ SAP employs a risk-based methodology to support planning, mitigation, and countermeasures against potential threats. ↳ They integrate security into every aspect of their operations, from development to deployment, following industry standards like NIST and ISO. SAP ensures the security of customer data when using third-party LLMs through data anonymization, strict data sharing policies, robust technical measures, compliance with global data protection laws, ethical AI development, and comprehensive security governance. #sap #saptraining #zarantech #AI #LLM #DataSecurity #india #usa #technology Disclaimer: Image generated using AI tool.

Yesterday, the National Security Agency Artificial Intelligence Security Center published the joint Cybersecurity Information Sheet Deploying AI Systems Securely in collaboration with the Cybersecurity and Infrastructure Security Agency, the Federal Bureau of Investigation (FBI), the Australian Signals Directorate’s Australian Cyber Security Centre, the Canadian Centre for Cyber Security, the New Zealand National Cyber Security Centre, and the United Kingdom’s National Cyber Security Centre. Deploying AI securely demands a strategy that tackles AI-specific and traditional IT vulnerabilities, especially in high-risk environments like on-premises or private clouds. Authored by international security experts, the guidelines stress the need for ongoing updates and tailored mitigation strategies to meet unique organizational needs. 🔒 Secure Deployment Environment: * Establish robust IT infrastructure. * Align governance with organizational standards. * Use threat models to enhance security. 🏗️ Robust Architecture: * Protect AI-IT interfaces. * Guard against data poisoning. * Implement Zero Trust architectures. 🔧 Hardened Configurations: * Apply sandboxing and secure settings. * Regularly update hardware and software. 🛡️ Network Protection: * Anticipate breaches; focus on detection and quick response. * Use advanced cybersecurity solutions. 🔍 AI System Protection: * Regularly validate and test AI models. * Encrypt and control access to AI data. 👮 Operation and Maintenance: * Enforce strict access controls. * Continuously educate users and monitor systems. 🔄 Updates and Testing: * Conduct security audits and penetration tests. * Regularly update systems to address new threats. 🚨 Emergency Preparedness: * Develop disaster recovery plans and immutable backups. 🔐 API Security: * Secure exposed APIs with strong authentication and encryption. This framework helps reduce risks and protect sensitive data, ensuring the success and security of AI systems in a dynamic digital ecosystem. #cybersecurity #CISO #leadership

The German Federal Office for Information Security (BSI) has published the updated version of its report on "Generative AI Models - Opportunities and Risks for Industry and Authorities". See the report here: https://lnkd.in/gRvHMDqA The report categorizes risks of LLMs into three buckets. It assigns numbers to the risks (R1-R28) as well as to countermeasures to mitigate the risks (M1-M18). The 3 risk categories are: • Risks in the context of proper use of LLMs (R1 – R11); • Risks due to misuse of LLMs (R12 – R18), • Risks resulting from attacks on LLMs (R19 – R28) Both risks and countermeasures can arise at different stages in the lifecycle of an LLM: 1.) the planning phase, 2.) the data phase, 3.) the development phase where model parameters such as architecture and size get determined, or a pre-trained model is selected, 4.) the operation phase, including training and validation. The graphics below aim to highlight 1.) when in the LLM lifecycle risks emerge and 2.) at which stage countermeasures can be sensibly implemented. The report also includes a cross-reference table (see p. 25) to provide an overview of which countermeasures reduce the probability of occurrence or the extent of damage of which risks. >>> Important Areas of Focus Recommended by the Report: <<< Educate users about the capabilities and risks of Large Language Models (LLMs), including potential data leaks, misuse, and security vulnerabilities.    Testing: Thorough testing of LLMs and their applications is crucial, possibly including red teaming to simulate attacks or misuse scenarios. Handling Sensitive Data: Assume that any data accessible to LLMs during training or operation could be exposed to users. Manage sensitive data carefully and consider using techniques like Retrieval-Augmented Generation (RAG) to implement rights and role systems. Establishing Transparency: Ensure that developers and operators disclose risks, countermeasures, residual risks, and limitations to users clearly, enhancing the explainability of LLM outputs. Auditing of Inputs and Outputs: Implement filters to clean inputs and outputs to prevent unwanted actions and allow user verification and modification of outputs. Managing Prompt Injections: Address vulnerabilities to prompt injections, which manipulate LLM behavior, by restricting application rights and implementing robust security practices. Managing Training Data: Carefully select, acquire, and preprocess training data, ensuring sensitive data is securely managed. Developing Practical Expertise: Build practical expertise through experimentation with LLMs, like conducting proof-of-concept projects, to realistically assess their capabilities and limitations. #LLMs #risk #controls #GenAI

👀 So, you might've heard about the Chevrolet chatbot getting a bit... let's say, 'off-track'. 😅 It's a classic example of "easy to make, hard to master" when it comes to building LLM apps. https://lnkd.in/da_C9R-x 🔧 Sure, tools like LangChain🦜 make it a breeze to whip up an LLM chatbot. But Here's the catch: (Gen)AI security posture is not just a fancy term; it ought to be the backbone of your AI development. 🌐 🛡️ Here's my take on deploying to production a safer RAG app (and avoiding our own Chevy moments): 1️⃣ Prompt Engineering: It's not a silver bullet, but it's a start. Steering the AI away from potentially harmful outputs is crucial and can be done with some protective prompt engineering to the final prompt sent to the LLM. 2️⃣ User Input Scanners: Inspect user generated input that is eventually augmenting your core prompt. This helps to tackle crafty input manipulations. 3️⃣ Prompt Input Scanners:  Double-checking the final prompt before sending it the LLM. Open source tools like @LLM- Guard by Laiyer AI provide a comprehensive suite designed to reinforce the security framework of LLM applications. 4️⃣ Proven Models for RAG: Using tried and tested certain models dedicated to RAG can save you a lot of prompt engineering and coding. 👉 Remember, this list isn't exhaustive, and there's no magic shield for GenAI apps. Think of them as essential AI hygiene practices. They significantly improve your GenAI security posture, laying a stronger foundation for your app. 💬 Bottom line: 👀 The Chevrolet case? Can happen to anyone and It's a wake-up call. BTW It's worth noting the impressive commitment from the LangChain🦜 team. They've really gone all-in, dedicating substantial effort to enhancing safety. Over the past few months, there's been a tremendous push in refactoring their framework, all aimed at providing an infrastructure that's geared towards building more secure and reliable apps Disclaimer: The thoughts and opinions shared here are entirely my own and do not represent those of my employer or any other affiliated organizations.

We can now automate the red-teaming of an LLM. Using an LLM! Introducing Meta AI's new MART framework. As we now use large language models in our daily lives and AI models disseminate into our tech culture through platforms like Hugging Face's Spaces, their security is paramount. Traditional red-teaming of AI models is slow and expensive, but Meta AI's MART framework automates this process. It pits an adversarial model against a target model. Through an iterative process, the adversarial model generates malicious prompts and attempts to extract harmful responses from the target model. The adversarial model learns from the successful attempts, and the target model learns from the failed attempts. The results are pretty striking—MART boosts safety by up to 84.7%, approaching the performance of heavily red-teamed models while preserving core helpfulness. This scalable approach allows us to harness the power of LLMs to improve LLMs. Read more about how MART in this week's post in the AI in Security blog.

As I work with companies that are stopping #artificialintelligence projects for #Security concerns, almost every time the priority list we work with them on is the same: 1) Your #identity visibility needs to be your main inspection chain. Confirm with a review and a controlled test, eliminate gaps. 2) Harden and protect logs for your #AI resources. Use activity and audit log in Microsoft 365 and use well-architected practices for serverless and resources in #Azure. 3) #threatmodeling is not a 4-letter word. Sit down and brainstorm all the bad things you worry about. then ask, which do you have examples from other areas of the business to suggest are real? Which have the most impact? If you have more formal models and tools, great. If your team doesn't, we can bring some basics, it doesn't have to be complicated or fancy to use #risk to prioritize the list. 4) Look at your top X from the list and pretend that it was happening to you. Use industry tools like MITRE #ATLAS and #ATTCK to give form to the "how" if you aren't sure. At each step of the attack see if you can explain how and where your tools either would see and respond to the threat. Use that to plan configuration adjustments and enhancements. Implement the easy quickly and prioritize the complex by what changes get the most coverage upgrade vs your prioritized list. If the sounds complicated, first, it's really not. it's really about breaking down large problems or complex problems into small steps. This is also where my team and my colleagues Steve Combs and Sean Ahmadinejad can surround your team with expertise and automation to trace logs, highlight vulnerabilities, and help with the enhancement prioritization and setting a team definition of what "good enough" might be to move the #ai or #copilot project forward if it's #Microsoft365. Get started.

Threat Modelling and Risk Analysis for Large Language Model (LLM)-Powered Applications by Stephen Burabari Tete:https://lnkd.in/gvVd5dU2 1)This paper explores the threat modeling and risk analysis specifically tailored for LLM-powered applications. 2) Focusing on potential attacks like data poisoning, prompt injection, SQL injection, jailbreaking, and compositional injection, the author assesses their impact on security and proposes mitigation strategies. The author introduces a framework combining STRIDE and DREAD methodologies for proactive threat identification and risk assessment. #ai #artificialintelligence #llm #llmsecurity #riskmanagment #riskanalysis #threats #risks #defenses #security