Security Considerations When Using AI Frameworks

When AI Meets Security: The Blind Spot We Can't Afford Working in this field has revealed a troubling reality: our security practices aren't evolving as fast as our AI capabilities. Many organizations still treat AI security as an extension of traditional cybersecurity—it's not. AI security must protect dynamic, evolving systems that continuously learn and make decisions. This fundamental difference changes everything about our approach. What's particularly concerning is how vulnerable the model development pipeline remains. A single compromised credential can lead to subtle manipulations in training data that produce models which appear functional but contain hidden weaknesses or backdoors. The most effective security strategies I've seen share these characteristics: • They treat model architecture and training pipelines as critical infrastructure deserving specialized protection • They implement adversarial testing regimes that actively try to manipulate model outputs • They maintain comprehensive monitoring of both inputs and inference patterns to detect anomalies The uncomfortable reality is that securing AI systems requires expertise that bridges two traditionally separate domains. Few professionals truly understand both the intricacies of modern machine learning architectures and advanced cybersecurity principles. This security gap represents perhaps the greatest unaddressed risk in enterprise AI deployment today. Has anyone found effective ways to bridge this knowledge gap in their organizations? What training or collaborative approaches have worked?

Is your AI model actually safe? ....The answer is more complicated than a simple yes or no. Many treat AI models like standard open-source software, checking the creator license and functionality. But this is a dangerous oversimplification. The term Open Source itself is misleading here. Unlike software where you can inspect the source code "open" AI models are often just open weights a massive file of numbers. You can't see the training data or the process that created them, making them a black box that's impossible to fully verify or reproduce. This opacity creates a massive attack surface. Scans have found hundreds of thousands of issues, including malicious models designed to exfiltrate data. The threats are real and evolving. So how do we secure the un-securable? Focus on three layers: The Model Itself: Source from trusted providers and rigorously evaluate for vulnerabilities like prompt injection, the number 1 security risk for LLMs according to OWASP. Continuous benchmarking is non-negotiable . The Infrastructure: The software stack running the model is a critical vulnerability. A model even if safe is only as secure as the infrastructure it runs on. Enforce strict privilege controls and secure your inference toolchain. The Integration: How does the model interact with your systems? A helpful model given excessive agency can become an unknowing accomplice, manipulated to expose system vulnerabilities or leak data. The models are innocent. It is the context they are used in that creates the risk. Security isn't a one time check, it's a continuous process of evaluation monitoring and mitigation. It's time we started treating it that way. What's your biggest concern when deploying a local AI models? #AI #Safety

🤖 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

Is your team still treating AI systems exactly like regular software when it comes to security? 🤔 I've been digging into NIST's draft Cyber AI Profile (IR 8596), which I think is essential reading for any GRC professional. The comment period closed last Friday, and this guidance confirms something many of us have felt for a while: AI challenges some of the core assumptions behind our traditional security frameworks. Unlike typical software which behaves predictably AI models are probabilistic and keep evolving. That means we face a new class of risks that require us to rethink our approach. A few takeaways for those of us in GRC: 💡 1️⃣ Static Checklists Don't Cut It: Because AI behavior is less predictable, relying solely on fixed checklists risks missing important threats. The guidance encourages adopting risk models designed specifically for AI's unique uncertainties. 2️⃣ New Threats Require New Defenses: Attacks like prompt injection, data poisoning, and model extraction aren't simply variations of traditional threats like malware or SQL injection. These AI-specific risks call for tailored mitigation strategies. 3️⃣ Seeing Beyond Vendor Reports: A SOC 2 report isn't enough anymore. To truly understand AI security, you have to trace data lineage, model origins, and base models. That means gaining much deeper insight into the AI supply chain. 4️⃣ Keep an Eye on AI Models Continuously: The draft stresses ongoing monitoring to catch things like model drift, unexpected behavior, and adversarial manipulation as soon as they happen. For those guiding AI risk and compliance programs, this is a strong nudge to update your frameworks. It also reinforces my conviction that the future belongs to practitioners fluent in both AI's technical landscape and sound governance principles. Although the comment period has closed, I encourage you to review the draft. Understanding this guidance now will help you prepare for the compliance landscape that's taking shape. If you're wrestling with how to handle AI's probabilistic risks, I'd be glad to swap notes on what I'm learning. 🤝 Find the draft here --> https://lnkd.in/gzxHSsQb #AIGovernance #GRC #Cybersecurity #AIrisk #NIST #RiskManagement

⚠️ 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

The latest joint cybersecurity guidance from the NSA, CISA, FBI, and international partners outlines critical best practices for securing data used to train and operate AI systems recognizing data integrity as foundational to AI reliability. Key highlights include: • Mapping data-specific risks across all 6 NIST AI lifecycle stages: Plan and Design, Collect and Process, Build and Use, Verify and Validate, Deploy and Use, Operate and Monitor • Identifying three core AI data risks: poisoned data, compromised supply chain, and data drift for each with tailored mitigations • Outlining 10 concrete data security practices, including digital signatures, trusted computing, encryption with AES 256, and secure provenance tracking • Exposing real-world poisoning techniques like split-view attacks (costing as little as 60 dollars) and frontrunning poisoning against Wikipedia snapshots • Emphasizing cryptographically signed, append-only datasets and certification requirements for foundation model providers • Recommending anomaly detection, deduplication, differential privacy, and federated learning to combat adversarial and duplicate data threats • Integrating risk frameworks including NIST AI RMF, FIPS 204 and 205, and Zero Trust architecture for continuous protection Who should take note: • Developers and MLOps teams curating datasets, fine-tuning models, or building data pipelines • CISOs, data owners, and AI risk officers assessing third-party model integrity • Leaders in national security, healthcare, and finance tasked with AI assurance and governance • Policymakers shaping standards for secure, resilient AI deployment Noteworthy aspects: • Mitigations tailored to curated, collected, and web-crawled datasets and each with unique attack vectors and remediation strategies • Concrete protections against adversarial machine learning threats including model inversion and statistical bias • Emphasis on human-in-the-loop testing, secure model retraining, and auditability to maintain trust over time Actionable step: Build data-centric security into every phase of your AI lifecycle by following the 10 best practices, conducting ongoing assessments, and enforcing cryptographic protections. Consideration: AI security does not start at the model but rather it starts at the dataset. If you are not securing your data pipeline, you are not securing your AI.

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.

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

Dear AI and Cybersecurity Auditors, AI changes how risk enters your environment and expands your attack surface. Traditional cybersecurity controls no longer cover model behavior, training data, prompts, agents, and AI-driven decisions. This draft extends NIST CSF 2.0 into AI systems. It treats models, data, prompts, agents, and AI decisions as real cyber assets. It also addresses how attackers already use AI to scale speed, deception, and impact. Here is why this framework matters for security, risk, and audit leaders. 📌 AI expands the attack surface beyond infrastructure into training data, models, prompts, agents, and third-party AI services 📌 Governance shifts from IT ownership to enterprise accountability with clear risk ownership, oversight, and decision authority 📌 Traditional controls still apply, but AI requires added focus on model integrity, data provenance, output reliability, and human oversight 📌 The framework maps AI risk directly to CSF functions so teams avoid parallel AI security programs 📌 Defensive teams use AI to reduce alert fatigue, improve detection accuracy, and support faster incident response 📌 Adversaries already use AI for phishing, malware generation, social engineering, and automated attack orchestration 📌 Continuous monitoring extends beyond systems into model drift, hallucinations, and unexpected behavior 📌 Risk tolerance must account for AI failure modes, not only system outages or data loss 📌 Audit and assurance teams gain a structured way to test AI controls across Secure, Defend, and Thwart focus areas 📌 The profile supports assessment, control design, and executive reporting without adding unnecessary complexity AI security fails when teams treat AI as software. NIST IR 8596 reframes AI as a risk domain inside cybersecurity. If your organization builds, buys, or relies on AI, this profile gives you a practical path to govern, secure, and defend it with intent. #NIST #Cybersecurity #AIGovernance #AIRisk #AIControls #ITAudit #CyberRisk #AISecurity #GRC #CSF #CyberVerge ♻️ Share this with your team or repost so more professionals. 👉Follow Nathaniel Alagbe for more.

Today, NIST released the initial preliminary draft of the Cybersecurity Framework Profile for Artificial Intelligence (Cyber AI Profile), a community profile built on NIST CSF 2.0 to help organizations manage cybersecurity risk in an AI-driven world. A key section of this draft is Section 2.1, which introduces three Focus Areas that explain how AI and cybersecurity intersect in practice: 1. Securing AI System Components (Secure) AI systems introduce new assets that must be secured; models, training data, prompts, agents, pipelines, and deployment environments. This focus area emphasizes treating AI components as first-class cybersecurity assets, integrating them into governance, risk assessments, protection controls, and monitoring processes. It reinforces that AI risk should not be siloed from enterprise cybersecurity risk management. 2. Conducting AI-Enabled Cyber Defense (Defend) AI is not just something to protect, it is also a powerful defensive capability. This area focuses on using AI to enhance detection, analytics, automation, and response across security operations. At the same time, it recognizes the risks of over-reliance on automation, model integrity concerns, and the need for human oversight when AI supports security decision-making. 3. Thwarting AI-Enabled Cyber Attacks (Thwart) Adversaries are increasingly using AI to scale phishing, evade detection, and automate attacks. This focus area addresses how organizations must anticipate and counter AI-enabled threats by building resilience, improving detection of AI-driven attack patterns, and preparing for a rapidly evolving threat landscape where AI is weaponized. Why This Matters Together, Secure, Defend, and Thwart provide a practical structure for aligning AI initiatives with existing cybersecurity programs. By mapping AI-specific considerations to CSF 2.0 outcomes (Govern, Identify, Protect, Detect, Respond, Recover), the Cyber AI Profile helps organizations integrate AI security into familiar risk management practices. This is a preliminary draft, and NIST is seeking public feedback through January 30, 2026. If your organization is building, deploying, or defending with AI, now is the time to review and contribute. 🔗 https://lnkd.in/e-ETZXH8