AI Security Measures for Risk Management

⚠️ 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

As organizations transition from pilots to enterprise-wide deployment of Generative and Agentic AI, it's crucial to recognize that GAI risks differ significantly from traditional software risks. Towards that, it is important to go back to basics and this publication from 2024 by National Institute of Standards and Technology (NIST)'s Generative AI Profile does a great job! 🌐 Here are the four highest-impact risks and the mitigation actions every organization should implement:- 1. Systemic Risk: Algorithmic Monocultures & Ecosystem-Level Failures When multiple industries depend on the same foundation models, a single unexpected model behavior can lead to correlated failures across the ecosystem. ⚡ Mitigation: - - Build model diversity and avoid single-model dependencies. - Maintain fallback systems and contingency workflows. - Apply stress tests that simulate sector-wide shocks. 2. Human-Originating Risks (Misuse, Over-Trust, Manipulation) Many GAI incidents stem from human behavior, including misuse, over-reliance, indirect prompt injection, and flawed assumptions. ⚡ Mitigation:- - Implement continuous user education on limitations and safe use. - Enforce access controls, privilege separation, and plugin vetting. - Maintain audit trails and logging to identify misuse early. 3. Content Integrity Risks (Hallucinations, Synthetic Media, Provenance Failure) GAI increases the scale and believability of fabricated content, from medical misinformation to deepfake-enabled harms. ⚡ Mitigation:- - Invest in content provenance, watermarking, and metadata tracking. - Require pre-deployment testing for hallucination profiles across contexts. - Use cross-model verification before high-stakes outputs are acted upon. 4. Security Risks (Prompt Injection, Data Leakage, Model Extraction) NIST highlights increasingly sophisticated attack surfaces unique to LLMs: indirect prompt injection, data extraction, and plugin-initiated compromise. ⚡ Mitigation:- - Apply secure-by-design reviews for all LLM integration points. - Red-team regularly using GAI-specific attack methods. - Log inputs/outputs via incident-ready documentation so breaches can be traced. 🔐 The bottom line:- AI risk management is not a technical afterthought, it is now a core capability. Organizations that operationalize governance, provenance, testing, and incident disclosure (NIST’s four focus pillars) will be the ones that deploy AI safely and at scale. 💬 If you’d like to explore Gen AI and Agentic AI risks, practical mitigation strategies, or how to operationalize the NIST AI RMF for your organization, feel free to comment or DM. Let’s build safer AI systems together! #AI #GenAI #AIGovernance #NIST #AIRMF #RiskManagement #AITrust #ResponsibleAI #AILeadership

🔐 𝗘𝘅𝗰𝗶𝘁𝗲𝗱 𝘁𝗼 𝘀𝗵𝗮𝗿𝗲 𝘀𝗼𝗺𝗲𝘁𝗵𝗶𝗻𝗴 𝗜’𝘃𝗲 𝗯𝗲𝗲𝗻 𝘄𝗼𝗿𝗸𝗶𝗻𝗴 𝗼𝗻 𝗳𝗼𝗿 𝘁𝗵𝗲 𝗴𝗹𝗼𝗯𝗮𝗹 𝗖𝘆𝗯𝗲𝗿 𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆 𝗰𝗼𝗺𝗺𝘂𝗻𝗶𝘁𝘆! I’m glad to release the 𝗖𝗜𝗦𝗢’𝘀 𝗗𝗲𝗳𝗶𝗻𝗶𝘁𝗶𝘃𝗲 𝗚𝘂𝗶𝗱𝗲 𝘁𝗼 𝗔𝗜 𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆 - comprehensive resource designed specifically for security leaders navigating the rapidly evolving world of artificial intelligence threats, risks, and defences. 🤖 𝗪𝗵𝘆 𝘁𝗵𝗶𝘀 𝗴𝘂𝗶𝗱𝗲, 𝗮𝗻𝗱 𝘄𝗵𝘆 𝗻𝗼𝘄? AI is no longer an emerging technology — it is core enterprise infrastructure. Yet most security frameworks were designed before machine learning became a business-critical asset. That gap is where adversaries are operating today. “𝗘𝘃𝗲𝗿𝘆𝗼𝗻𝗲 𝗶𝘀 𝗿𝘂𝘀𝗵𝗶𝗻𝗴 𝗶𝗻𝘁𝗼 𝗔𝗜... 𝗮𝗹𝗺𝗼𝘀𝘁 𝗻𝗼 𝗼𝗻𝗲 𝗶𝘀 𝘀𝗲𝗰𝘂𝗿𝗶𝗻𝗴 𝗶𝘁 𝗽𝗿𝗼𝗽𝗲𝗿𝗹𝘆.” 📘 𝗪𝗵𝗮𝘁’𝘀 𝗶𝗻𝘀𝗶𝗱𝗲: 🛡️ AI Threat Landscape & Attack Taxonomy (mapped to MITRE ATLAS) ⚖️ Risk Assessment Framework & AI Risk Heat Matrix 🏗️ AI Security Architecture — Defense-in-Depth 📋 Governance, Compliance & Regulatory Landscape (EU AI Act, NIST AI RMF, ISO 42001) 🔄 MLSecOps & Secure AI Development Lifecycle 🚨 AI-Specific Incident Response Playbooks 🔍 Security Architecture Review Methodology & Checklists 🧪 Security Testing Standards — OWASP LLM Top 10, Tools & Cadence 🤝 Vendor & Third-Party AI Risk Management 📊 AI Security Metrics, Maturity Model & 12-Month Roadmap 💡 𝗞𝗲𝘆 𝗶𝗻𝘀𝗶𝗴𝗵𝘁 𝗳𝗼𝗿 𝗖𝗜𝗦𝗢𝘀: Attackers are already using AI. Your defence must be AI-native too. This guide is circulated freely for the Global CISO Community — because great security knowledge should be shared, not siloed. “𝗜𝗳 𝘆𝗼𝘂’𝗿𝗲 𝗮 𝗱𝗲𝗮𝗹𝗶𝗻𝗴 𝘄𝗶𝘁𝗵 𝗔𝗜, 𝘁𝗵𝗶𝘀 𝗴𝘂𝗶𝗱𝗲 𝗶𝘀 𝗳𝗼𝗿 𝘆𝗼𝘂.” 📩 Drop a comment or DM me if you’d liked it, Let’s raise the bar for AI security together. #CyberSecurity #AISecurity #CISO #AIRisk #LLMSecurity #OWASP #MachineLearning #InfoSec #SecurityLeadership #MLSecOps #ThreatIntelligence #GenerativeAI #ZeroTrust #CISOCommunity #DigitalRisk #SecurityArchitecture #AIGovernance #CyberResilience #SecurityTesting #RiskManagement

ISO/IEC 27090 is soon to be published. After reviewing the final draft, one thing stands out: AI is not just introducing new risks. It is forcing organisations to define entirely new policy domains. Here are the key high-level AI security policies emerging from the standard: 🔹 AI Governance Establish ownership, maintain an inventory of AI systems (AIBOM), and manage risk across the lifecycle. 🔹 Data Usage & Minimisation Define what data can be used in AI, minimise data exposure, control retention, and apply privacy-preserving techniques. 🔹 Zero Trust for AI Adopt “never trust, always verify” for both users and AI systems, with strict identity and least privilege controls. 🔹 AI Lifecycle Security Apply secure engineering practices from development to deployment, including model continuous input/output validation and testing. 🔹 Model Behaviour & Safety Controls Set guardrails to manage unwanted behaviour, prevent overreliance, and limit excessive autonomy. 🔹 Human Oversight Define when human review is required to maintain accountability and avoid “out-of-the-loop” risk. 🔹 Supply Chain & Model Provenance Track where models and data come from, and manage risks across increasingly complex AI supply chains. 🔹 Monitoring & Validation Log, monitor, and continuously validate AI behaviour to detect drift, anomalies, and attacks. 🔹 Threat Modelling & Red Teaming Actively test AI systems against adversarial scenarios such as prompt injection and data poisoning. 🔹 AI-Specific Threat Protection Recognise that AI introduces new attack surfaces and requires controls beyond traditional cybersecurity. The shift is clear: 👉 We are no longer just securing systems 👉 We are securing data flows, model behaviour, and decision-making itself Organisations must translate this into clear, enforceable policies aligned to their AI architecture, to scale safely. Curious how others are aligning to emerging standards like ISO 27090.

𝐀𝐈 𝐒𝐞𝐜𝐮𝐫𝐢𝐭𝐲 𝐈𝐬 𝐧𝐨𝐭 𝐎𝐧𝐞 𝐓𝐨𝐨𝐥, 𝐈𝐭 𝐢𝐬 𝐚 𝐒𝐭𝐚𝐜𝐤 Buying one security product and calling your AI "secure" is like locking the front door while leaving every window open. Real AI security is six layers deep: 𝐋𝐀𝐘𝐄𝐑 𝟏: 𝐈𝐃𝐄𝐍𝐓𝐈𝐓𝐘 𝐀𝐍𝐃 𝐀𝐂𝐂𝐄𝐒𝐒 Purpose: Control who can access AI systems, models, and data. What it includes: Model APIs, internal AI tools, agent-level permissions. Key controls: - Role-based and attribute-based access - Zero-trust architecture - API authentication No identity layer means anyone or any agent can reach your models. 𝐋𝐀𝐘𝐄𝐑 𝟐: 𝐃𝐀𝐓𝐀 𝐏𝐑𝐎𝐓𝐄𝐂𝐓𝐈𝐎𝐍 Purpose: Safeguard sensitive organizational data before it is used by AI models. What it protects: Personally identifiable information, financial records, internal business data. Key controls: - Data masking - Tokenization - Encryption (in transit and at rest) 𝐋𝐀𝐘𝐄𝐑 𝟑: 𝐏𝐑𝐎𝐌𝐏𝐓 𝐀𝐍𝐃 𝐈𝐍𝐏𝐔𝐓 𝐒𝐄𝐂𝐔𝐑𝐈𝐓𝐘 Purpose: Defend AI models against malicious or manipulated inputs. Risks handled: Prompt injection attacks, data leakage through prompts, jailbreak attempts. Key controls: - Input validation - Prompt filtering - Policy enforcement - Rate limiting This is the layer most teams skip and where most AI-specific attacks happen. 𝐋𝐀𝐘𝐄𝐑 𝟒: 𝐆𝐎𝐕𝐄𝐑𝐍𝐀𝐍𝐂𝐄 𝐀𝐍𝐃 𝐂𝐎𝐌𝐏𝐋𝐈𝐀𝐍𝐂𝐄 Purpose: Ensure AI systems comply with regulations and internal policies. Framework coverage: GDPR, EU AI Act, ISO 42001. Key controls: - Audit logging - Risk classification - Decision traceability - Policy enforcement 𝐋𝐀𝐘𝐄𝐑 𝟓: 𝐎𝐔𝐓𝐏𝐔𝐓 𝐕𝐀𝐋𝐈𝐃𝐀𝐓𝐈𝐎𝐍 Purpose: Verify AI-generated responses before they are used or acted upon. Risks addressed: Hallucinated outputs, compliance violations, unsafe or harmful responses. Key controls: - Fact-checking mechanisms - Policy validation - Output moderation 𝐋𝐀𝐘𝐄𝐑 𝟔: 𝐌𝐎𝐍𝐈𝐓𝐎𝐑𝐈𝐍𝐆 𝐀𝐍𝐃 𝐎𝐁𝐒𝐄𝐑𝐕𝐀𝐁𝐈𝐋𝐈𝐓𝐘 Purpose: Continuously track AI system behavior in production environments. What it monitors: Usage patterns, response accuracy, model drift, latency. Key controls: - Behavior tracking - Audit logs - Performance monitoring 𝐖𝐇𝐄𝐑𝐄 𝐓𝐄𝐀𝐌𝐒 𝐆𝐎 𝐖𝐑𝐎𝐍𝐆 They invest heavily in Layer 1 (identity and access) and ignore Layers 3 and 5 (prompt security and output validation).  The result is a system that authenticates users perfectly but lets prompt injections and hallucinated outputs through unchecked. 𝐓𝐇𝐄 𝐏𝐑𝐈𝐍𝐂𝐈𝐏𝐋𝐄 AI security is a stack, not a tool.  Six layers, each protecting a different attack surface.  Miss one and the others can not compensate. 𝐇𝐨𝐰 𝐦𝐚𝐧𝐲 𝐨𝐟 𝐭𝐡𝐞𝐬𝐞 𝐬𝐢𝐱 𝐥𝐚𝐲𝐞𝐫𝐬 𝐝𝐨𝐞𝐬 𝐲𝐨𝐮𝐫 𝐀𝐈 𝐬𝐲𝐬𝐭𝐞𝐦 𝐜𝐮𝐫𝐫𝐞𝐧𝐭𝐥𝐲 𝐜𝐨𝐯𝐞𝐫? ♻️ Repost this to help your network get started ➕ Follow Sivasankar Natarajan for more #EnterpriseAI #AgenticAI #AIAgents

🤖 AI & Cyber Risk Governance How to adapt risk registers, controls, and policies for the AI era AI is no longer a future cyber risk. It is actively changing how attacks happen and how organizations use technology internally. GRC programs must adapt — now. 1️⃣ Update the Risk Register AI amplifies existing cyber risks by increasing speed, scale, and precision. Key risk scenarios to include: AI-generated phishing & social engineering Deepfake voice and video fraud AI-assisted malware & exploit discovery Prompt injection and data poisoning AI model leakage and misuse 👉 Reassess likelihood and impact — AI changes both. 2️⃣ Strengthen Security Controls Controls must protect data, systems, and AI models. Technical controls: AI-aware threat detection Deepfake detection mechanisms Secure AI APIs and integrations Input/output filtering for AI systems Governance controls: Human-in-the-loop for critical decisions Model validation and bias testing Access logging and continuous monitoring 3️⃣ Formalize AI Usage Policies Uncontrolled AI use creates Shadow AI risk. Essential policies: Acceptable use of AI tools Restrictions on sensitive data in prompts Ownership and accountability of AI outputs Third-party AI risk management If it’s not documented, it’s not governed. 4️⃣ Align with Existing GRC Frameworks AI governance extends current frameworks: ISO 27001 → AI as an information asset NIST RMF → AI-driven threat modeling AI RMF / ISO AI standards → AI risk assessment SOC 2 → Evidence of AI controls 🔑 Key Takeaway AI doesn’t replace cyber risks — it amplifies them. Organizations that govern AI proactively reduce fraud, compliance risk, and operational surprises. 💬 How is your organization adapting its GRC program for AI? #AI #CyberSecurity #GRC #RiskManagement #AIGovernance #ISO27001 #NIST #Compliance