How to Adapt Security Strategies for AI

Zero Trust Architecture for LLMs — Securing the Next Frontier of AI AI systems are powerful, but also risky. Large Language Models (LLMs) can expose sensitive data, misinterpret context, or be manipulated through prompt injection. That’s why Zero Trust for AI isn’t optional anymore — it’s essential. Here’s how a modern LLM stack can adopt a Zero Trust Architecture (ZTA) to stay secure from input to output. 1. Data Ingestion — Trust Nothing by Default 🔹Every input — whether human, application, or IoT sensor — must go through identity verification before login. 🔹 A policy engine evaluates user, device, and risk signals in real-time. No data flows unchecked. No implicit trust. 2. Identity and Access Management 🔹Implement Attribute-Based Access Control (ABAC) — access is granted based on who, what, and where. 🔹 Add Multi-Factor Authentication (MFA) and Just-in-Time provisioning to limit standing privileges. 🔹Combine these with a Zero Trust framework that authenticates every interaction — even inside your own network. 3. LLM Security Layer — Real-Time Defense LLMs are intelligent but vulnerable. They need a layered defense model that protects both inputs and outputs. This includes: 🔹Prompt filtering to prevent injection or manipulation 🔹Input validation to block malformed or unsafe data 🔹Data masking to remove sensitive information before processing 🔹Ethical guardrails to prevent biased or non-compliant responses 🔹Response filtering to ensure no sensitive or toxic output leaves the system This turns your LLM from a black box into a controlled, auditable system. 4. Core Zero Trust Principles for LLMs 🔹Verify explicitly — never assume identity or intent 🔹Assume breach — design as if every layer could be compromised 🔹Enforce least privilege — restrict what data, models, and prompts each actor can access When these principles are embedded into the model workflow, you achieve continuous verification — not one-time security. 5. Monitoring and Governance 🔹Security is not a one-time activity. 🔹Continuous policy configuration, monitoring, and threat detection keep your models aligned with compliance frameworks. 🔹Security policies evolve through a knowledge base that learns from incidents and new data. The result is a self-improving defense loop. => Why it Matters 🔹LLMs represent a new kind of attack surface — one that blends data, model logic, and user intent. 🔹Zero Trust ensures you control who interacts with your model, what they send, and what leaves the system. 🔹This mindset shifts AI from secure-perimeter thinking to secure-everywhere thinking. 🔹Every request is verified, every action is authorized, and every output is validated. How is your organization embedding Zero Trust principles into GenAI systems? Follow Rajeshwar D. for insights on AI/ML. #AI #LLM #ZeroTrust #CyberSecurity #GenAI #AIArchitecture #DataSecurity #PromptSecurity #AICompliance #AIGovernance

13 national cyber agencies from around the world, led by #ACSC, have collaborated on a guide for secure use of a range of "AI" technologies, and it is definitely worth a read! "Engaging with Artificial Intelligence" was written with collaboration from Australian Cyber Security Centre, along with the Cybersecurity and Infrastructure Security Agency (#CISA), FBI, NSA, NCSC-UK, CCCS, NCSC-NZ, CERT NZ, BSI, INCD, NISC, NCSC-NO, CSA, and SNCC, so you would expect this to be a tome, but it's only 15 pages! It is refreshing to see that the article is not solely focused on LLMs (eg. ChatGPT), but defines Artificial Intelligence to include Machine Learning, Natural Language Processing, and Generative AI (LLMs), while acknowledging there are other sub-fields as well. The challenges identified (with actual real-world examples!) are: 🚩 Data Poisoning of an AI Model: manipulating an AI model's training data, leading to incorrect, biased, or malicious outputs 🚩 Input Manipulation Attacks: includes prompt injection and adversarial examples, where malicious inputs are used to hijack AI model outputs or cause misclassifications 🚩 Generative AI Hallucinations: generating inaccurate or factually incorrect information 🚩 Privacy and Intellectual Property Concerns: challenges in ensuring the security of sensitive data, including personal and intellectual property, within AI systems 🚩 Model Stealing Attack: creating replicas of AI models using the outputs of existing systems, raising intellectual property and privacy issues The suggested mitigations include generic (but useful!) cybersecurity advice as well as AI-specific advice: 🔐 Implement cyber security frameworks 🔐 Assess privacy and data protection impact 🔐 Enforce phishing-resistant multi-factor authentication 🔐 Manage privileged access on a need-to-know basis 🔐 Maintain backups of AI models and training data 🔐 Conduct trials for AI systems 🔐 Use secure-by-design principles and evaluate supply chains 🔐 Understand AI system limitations 🔐 Ensure qualified staff manage AI systems 🔐 Perform regular health checks and manage data drift 🔐 Implement logging and monitoring for AI systems 🔐 Develop an incident response plan for AI systems This guide is a great practical resource for users of AI systems. I would interested to know if there are any incident response plans specifically written for AI systems - are there any available from a reputable source?

🤖 𝐄𝐯𝐞𝐫𝐲𝐨𝐧𝐞’𝐬 𝐭𝐚𝐥𝐤𝐢𝐧𝐠 𝐚𝐛𝐨𝐮𝐭 𝐀𝐈 𝐚𝐝𝐨𝐩𝐭𝐢𝐨𝐧 – 𝐛𝐮𝐭 𝐡𝐚𝐫𝐝𝐥𝐲 𝐚𝐧𝐲𝐨𝐧𝐞 𝐢𝐬 𝐭𝐚𝐥𝐤𝐢𝐧𝐠 𝐚𝐛𝐨𝐮𝐭 𝐀𝐈 𝐬𝐞𝐜𝐮𝐫𝐢𝐭𝐲. 🔐 As a CISO, I see the rapid rollout of AI tools across organizations. But what often gets overlooked are the unique security risks these systems introduce. Unlike traditional software, AI systems create entirely new attack surfaces like: ⚠️ 𝐃𝐚𝐭𝐚 𝐩𝐨𝐢𝐬𝐨𝐧𝐢𝐧𝐠: Just a few manipulated data points can alter model behavior in subtle but dangerous ways. ⚠️ 𝐏𝐫𝐨𝐦𝐩𝐭 𝐢𝐧𝐣𝐞𝐜𝐭𝐢𝐨𝐧: Malicious inputs can trick models into revealing sensitive data or bypassing safeguards. ⚠️ 𝐒𝐡𝐚𝐝𝐨𝐰 𝐀𝐈: Unofficial tools used without oversight can undermine compliance and governance entirely. We urgently need new ways of thinking and structured frameworks to embed security from the very beginning. 📘 A great starting point is the new 𝐒𝐀𝐈𝐋 (𝐒𝐞𝐜𝐮𝐫𝐞 𝐀𝐈 𝐋𝐢𝐟𝐞𝐜𝐲𝐜𝐥𝐞) Framework whitepaper by Pillar Security. It provides actionable guidance for integrating security across every phase of the AI lifecycle from planning and development to deployment and monitoring. 🔍 𝐖𝐡𝐚𝐭 𝐈 𝐩𝐚𝐫𝐭𝐢𝐜𝐮𝐥𝐚𝐫𝐥𝐲 𝐯𝐚𝐥𝐮𝐞: ✅ More than 𝟕𝟎 𝐀𝐈-𝐬𝐩𝐞𝐜𝐢𝐟𝐢𝐜 𝐫𝐢𝐬𝐤𝐬, mapped and categorized ✅ A clear phase-based structure: Plan – Build – Test – Deploy – Operate – Monitor ✅ Alignment with current standards like ISO 42001, NIST AI RMF and the OWASP Top 10 for LLMs 👉 Read the full whitepaper here: https://lnkd.in/ebtbztQC How are you approaching AI risk in your organization? Have you already started implementing a structured AI security framework? #AIsecurity #CISO #SAILframework #SecureAI #Governance #MLops #Cybersecurity #AIrisks

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

⚠️ Most companies treat AI agents like chatbots. But most of us know that this means - it’s only a matter of time before it causes a major security incident. Here’s what i experienced at an example company: An AI agent monitoring cloud infrastructure. It doesn’t just respond. It observes, reasons, and executes actions across multiple systems. That means it can: - Read logs - Trigger deployments - Update tickets - Execute scripts All without direct human prompting. My approach after years in cybersecurity & AI is to use a 5-Layer Security Model when reviewing AI agent security: 1️⃣ Prompt Layer Where instructions enter the system (user messages, docs, tickets). ⚠️ Risk: Prompt injection – hidden instructions can trick the agent into executing real commands. 2️⃣ Knowledge / Memory Layer Agents retrieve context from logs, docs, or vector databases and connects to internal resources with potential sensitive information. ⚠️ Risk: Data poisoning – malicious content can influence future decisions. 3️⃣ Reasoning Layer (LLM) Application comes in contact with you LLM - where the model decides what to do. ⚠️ Risk: Hallucinations/unintentional leakage – confident but incorrect suggestions could trigger unsafe actions. 4️⃣ Tool / Action Layer AI Agents interact with APIs, CI/CD pipelines, databases, and infra. ⚠️ Risk: Unauthorized execution – a single manipulated prompt could impact production systems. 5️⃣ Infrastructure / Control Plane The container, runtime, identities, secrets, and policy engines live here. ⚠️ Risk: Agent hijacking – compromise this layer, and attackers control every decision. 💡 Rule of thumb: Never allow an AI agent to perform an action you cannot observe, audit, or override. Curious — how are you approaching AI agent security? #aisecurity #ai

One of the most interesting aspects of my last few roles, including my current work at Humain, is operating at the intersection of AI and advanced security/encryption techniques from zero-knowledge proof systems to the extension of Zero Trust principles into the agentic world. In traditional Zero Trust, we authenticate users and devices. In the agentic world, the “user” could be an autonomous agent — a system that reasons, acts, and interacts with data and other agents, often at machine speed. That changes everything. To secure this new ecosystem, Zero Trust must evolve from static identity verification to dynamic trust orchestration, where every action, decision, and data exchange is continuously verified, contextual, and cryptographically enforced. 1. Agent Identity and Attestation Every agent must have a verifiable, cryptographically signed identity and prove its integrity at runtime; not just who you are, but what you’re running: the model, weights, policy context, and data provenance. 2. Intent-Aware Policy Enforcement Access control must become intent-aware, so agents act only within bounded policy domains defined by explicit goals, permissions, and ethical constraints — continuously verified by embedded governance logic. 3. Least Privilege and Time-Bound Access Agents must operate under least privilege, with access granted only for the minimum scope and durationrequired. In fast-moving agentic environments, time-limited trust becomes an essential safeguard. 4. Assumed Breach and Blast Radius Containment We must assume some agents or environments will be compromised. Security design should minimise impact through microsegmentation, strict trust boundaries, and dynamic reassessment of communication between agents. 5. Encrypted Cognition As models process sensitive data, confidential AI becomes essential where combining homomorphic encryption, secure enclaves, and multi-party computation can ensure that the model cannot “see” the data it processes. Zero Trust now extends into the reasoning process itself. 6. Adaptive Trust Graphs Agents, services, and humans form dynamic trust graphs that evolve based on behaviour and context. Continuous telemetry and anomaly detection allow these graphs to adjust privileges in real time based on risk. 7. Cryptographic Provenance Every output, decision, summary, or recommendation must be traceable back to the data, model, and policy that produced it. Provenance becomes the new perimeter. 8. Autonomous Audit and Forensics Every action should be self-auditing, cryptographically signed, and non-repudiable forming the foundation for verifiable operations and compliance. 9. Machine-to-Machine Governance As agents begin to negotiate, transact, and collaborate, Zero Trust must extend into inter-agent diplomacy, embedding ethics, accountability, and policy directly into machine communication. If you’re working on AI security, agent governance, or confidential computation, I’d love to connect.

The Cybersecurity and Infrastructure Security Agency (CISA), together with other organizations, published "Principles for the Secure Integration of Artificial Intelligence in Operational Technology (OT)," providing a comprehensive framework for critical infrastructure operators evaluating or deploying AI within industrial environments. This guidance outlines four key principles to leverage the benefits of AI in OT systems while reducing risk: 1. Understand the unique risks and potential impacts of AI integration into OT environments, the importance of educating personnel on these risks, and the secure AI development lifecycle.  2. Assess the specific business case for AI use in OT environments and manage OT data security risks, the role of vendors, and the immediate and long-term challenges of AI integration 3. Implement robust governance mechanisms, integrate AI into existing security frameworks, continuously test and evaluate AI models, and consider regulatory compliance.  4. Implement oversight mechanisms to ensure the safe operation and cybersecurity of AI-enabled OT systems, maintain transparency, and integrate AI into incident response plans. The guidance recommends addressing AI-related risks in OT environments by: • Conducting a rigorous pre-deployment assessment. • Applying AI-aware threat modeling that includes adversarial attacks, model manipulation, data poisoning, and exploitation of AI-enabled features. • Strengthening data governance by protecting training and operational data, controlling access, validating data quality, and preventing exposure of sensitive engineering information. • Testing AI systems in non-production environments using hardware-in-the-loop setups, realistic scenarios, and safety-critical edge cases before deployment. • Implementing continuous monitoring of AI performance, outputs, anomalies, and model drift, with the ability to trace decisions and audit system behavior. • Maintaining human oversight through defined operator roles, escalation paths, and controls to verify AI outputs and override automated actions when needed. • Establishing safe-failure and fallback mechanisms that allow systems to revert to manual control or conventional automation during errors, abnormal behavior, or cyber incidents. • Integrating AI into existing cybersecurity and functional safety processes, ensuring alignment with risk assessments, change management, and incident response procedures. • Requiring vendor transparency on embedded AI components, data usage, model behavior, update cycles, cybersecurity protections, and conditions for disabling AI capabilities. • Implementing lifecycle management practices such as periodic risk reviews, model re-evaluation, patching, retraining, and re-testing as systems evolve or operating environments change.

Most AI breaches won't look like hacks. They'll look like trust. I've been in IT for 15 years. Built AI systems long enough to spot the difference between hype and frameworks that actually hold up in production. When Cisco released its AI Security Framework, I read the entire thing. Most security docs treat AI like traditional software. Patch it. Firewall it. Done. Cisco gets something most enterprises don't: security and safety aren't two teams arguing after an incident. They're one system. 19 attacker objectives. 40 techniques. Over 100 concrete failure modes. This matters because most AI breaches won't look like classic hacks: 𝗚𝗼𝗮𝗹 𝗵𝗶𝗷𝗮𝗰𝗸𝗶𝗻𝗴. Your agent gets manipulated into pursuing objectives you never intended. 𝗧𝗼𝗼𝗹 𝘀𝗽𝗼𝗼𝗳𝗶𝗻𝗴. An attacker substitutes a legitimate tool with a malicious one. Your agent can't tell the difference. 𝗣𝗼𝗶𝘀𝗼𝗻𝗲𝗱 𝗱𝗲𝗽𝗲𝗻𝗱𝗲𝗻𝗰𝗶𝗲𝘀. That open-source model you pulled from Hugging Face? Compromised before you downloaded it. 𝗤𝘂𝗶𝗲𝘁 𝗱𝗮𝘁𝗮 𝗲𝘅𝗳𝗶𝗹𝘁𝗿𝗮𝘁𝗶𝗼𝗻. Through agents you trusted. No alarms. No alerts. Just steady leakage. If you're deploying agents without guardrails, auditability, and supply chain controls, you're not moving fast. You're building future incidents. The rollout plan that actually works: 𝟭. 𝗧𝗿𝗲𝗮𝘁 𝗮𝗴𝗲𝗻𝘁𝘀 𝗹𝗶𝗸𝗲 𝗻𝗲𝘄 𝗵𝗶𝗿𝗲𝘀 Same access controls. Same permissions review. Same principle of least privilege. 𝟮. 𝗔𝘂𝗱𝗶𝘁 𝘆𝗼𝘂𝗿 𝘁𝗼𝗼𝗹 𝗰𝗵𝗮𝗶𝗻 Every tool your agent can call is an attack surface. If you can't explain what it does and why your agent needs it, remove it. 𝟯. 𝗕𝘂𝗶𝗹𝗱 𝗼𝗯𝘀𝗲𝗿𝘃𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗳𝗿𝗼𝗺 𝗱𝗮𝘆 𝗼𝗻𝗲 Every decision. Every action. Every output. You need receipts. 𝟰. 𝗜𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁 𝗴𝘂𝗮𝗿𝗱𝗿𝗮𝗶𝗹𝘀, 𝗻𝗼𝘁 𝗷𝘂𝘀𝘁 𝗴𝘂𝗶𝗱𝗲𝗹𝗶𝗻𝗲𝘀 Prompts can be jailbroken. Hard constraints in code. Rate limits. Output validation. 𝟱. 𝗣𝗹𝗮𝗻 𝗳𝗼𝗿 𝗳𝗮𝗶𝗹𝘂𝗿𝗲 Kill switches. Rollback procedures. Not if your agent fails. When. While enterprises debate AI governance frameworks, attackers are studying how agents work. The gap between "we're exploring AI security" and "we have production guardrails" is where breaches happen. Most AI systems will fail. The question is whether you designed for that failure or pretended it wouldn't happen. Build like you expect to be attacked. Because you will be. What's your current guardrail strategy for agents in production?

☢️Manage Third-Party AI Risks Before They Become Your Problem☢️ AI systems are rarely built in isolation as they rely on pre-trained models, third-party datasets, APIs, and open-source libraries. Each of these dependencies introduces risks: security vulnerabilities, regulatory liabilities, and bias issues that can cascade into business and compliance failures. You must move beyond blind trust in AI vendors and implement practical, enforceable supply chain security controls based on #ISO42001 (#AIMS). ➡️Key Risks in the AI Supply Chain AI supply chains introduce hidden vulnerabilities: 🔸Pre-trained models – Were they trained on biased, copyrighted, or harmful data? 🔸Third-party datasets – Are they legally obtained and free from bias? 🔸API-based AI services – Are they secure, explainable, and auditable? 🔸Open-source dependencies – Are there backdoors or adversarial risks? 💡A flawed vendor AI system could expose organizations to GDPR fines, AI Act nonconformity, security exploits, or biased decision-making lawsuits. ➡️How to Secure Your AI Supply Chain 1. Vendor Due Diligence – Set Clear Requirements 🔹Require a model card – Vendors must document data sources, known biases, and model limitations. 🔹Use an AI risk assessment questionnaire – Evaluate vendors against ISO42001 & #ISO23894 risk criteria. 🔹Ensure regulatory compliance clauses in contracts – Include legal indemnities for compliance failures. 💡Why This Works: Many vendors haven’t certified against ISO42001 yet, but structured risk assessments provide visibility into potential AI liabilities. 2️. Continuous AI Supply Chain Monitoring – Track & Audit 🔹Use version-controlled model registries – Track model updates, dataset changes, and version history. 🔹Conduct quarterly vendor model audits – Monitor for bias drift, adversarial vulnerabilities, and performance degradation. 🔹Partner with AI security firms for adversarial testing – Identify risks before attackers do. (Gemma Galdon Clavell, PhD , Eticas.ai) 💡Why This Works: AI models evolve over time, meaning risks must be continuously reassessed, not just evaluated at procurement. 3️. Contractual Safeguards – Define Accountability 🔹Set AI performance SLAs – Establish measurable benchmarks for accuracy, fairness, and uptime. 🔹Mandate vendor incident response obligations – Ensure vendors are responsible for failures affecting your business. 🔹Require pre-deployment model risk assessments – Vendors must document model risks before integration. 💡Why This Works: AI failures are inevitable. Clear contracts prevent blame-shifting and liability confusion. ➡️ Move from Idealism to Realism AI supply chain risks won’t disappear, but they can be managed. The best approach? 🔸Risk awareness over blind trust 🔸Ongoing monitoring, not just one-time assessments 🔸Strong contracts to distribute liability, not absorb it If you don’t control your AI supply chain risks, you’re inheriting someone else’s. Please don’t forget that.

In the landscape of AI, robust governance, risk, and security frameworks are essential to manage various risks. However, a silent yet potent threat looms: Prompt Injection. Prompt Injection exploits the design of large language models (LLMs), which treat instructions and data within the same context window. Natural language sanitization is nearly impossible, highlighting the need for architectural defenses. If these defenses are not implemented correctly, they pose significant threats to an organization's reputation, compliance, and bottom line. For instance, a chatbot designed to handle client queries 24/7 could be manipulated into revealing company secrets, generating offensive content, or connecting with internal systems. To address these challenges, a Defense-in-Depth approach is crucial for implementing AI use cases: 1. Zero-Trust for AI: Assume every prompt is hostile and establish mechanisms to validate all inputs. 2. Prompt Firewalls: Implement pattern recognition for both incoming prompts and outgoing responses. 3. Architectural Separation: Ensure no LLM has direct access to databases and APIs. It should communicate with your data without direct interaction, with an intermediate layer that includes all necessary security controls. 4. AI Bodyguards: Leverage specialized security AI models to screen prompts and responses for malicious intent. 5. Continuous Stress Testing: Engage "red teams" to actively attempt to breach your AI's defenses, identifying weaknesses before real attackers do. The future of AI is promising, but only if it is secure. Consider how you are fortifying your AI adoption. #riskmanagement #AIGovernance #cybersecurity