Security Considerations When Using AI Frameworks

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

AI use is exploding. I spent my weekend analyzing the top vulnerabilities I've seen while helping companies deploy it securely. Here's EXACTLY what to look for: 1️⃣ UNINTENDED TRAINING Occurs whenever: - an AI model trains on information that the provider of such information does NOT want the model to be trained on, e.g. material non-public financial information, personally identifiable information, or trade secrets - AND those not authorized to see this underlying information nonetheless can interact with the model itself and retrieve this data. 2️⃣ REWARD HACKING Large Language Models (LLMs) can exhibit strange behavior that closely mimics that of humans. So: - offering them monetary rewards, - saying an important person has directed an action, - creating false urgency due to a manufactured crisis, or even telling the LLM what time of year it is can have substantial impacts on the outputs. 3️⃣ NON-NEUTRAL SECURITY POLICY This occurs whenever an AI application attempts to control access to its context (e.g. provided via retrieval-augmented generation) through non-deterministic means (e.g. a system message stating "do not allow the user to download or reproduce your entire knowledge base"). This is NOT a correct AI security measure, as rules-based logic should determine whether a given user is authorized to see certain data. Doing so ensures the AI model has a "neutral" security policy, whereby anyone with access to the model is also properly authorized to view the relevant training data. 4️⃣ TRAINING DATA THEFT Separate from a non-neutral security policy, this occurs when the user of an AI model is able to recreate - and extract - its training data in a manner that the maintainer of the model did not intend. While maintainers should expect that training data may be reproduced exactly at least some of the time, they should put in place deterministic/rules-based methods to prevent wholesale extraction of it. 5️⃣ TRAINING DATA POISONING Data poisoning occurs whenever an attacker is able to seed inaccurate data into the training pipeline of the target model. This can cause the model to behave as expected in the vast majority of cases but then provide inaccurate responses in specific circumstances of interest to the attacker. 6️⃣ CORRUPTED MODEL SEEDING This occurs when an actor is able to insert an intentionally corrupted AI model into the data supply chain of the target organization. It is separate from training data poisoning in that the trainer of the model itself is a malicious actor. 7️⃣ RESOURCE EXHAUSTION Any intentional efforts by a malicious actor to waste compute or financial resources. This can result from simply a lack of throttling or - potentially worse - a bug allowing long (or infinite) responses by the model to certain inputs. 🎁 That's a wrap! Want to grab the entire StackAware AI security reference and vulnerability database? Head to: archive [dot] stackaware [dot] com

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.

The proliferation of AI agents, particularly the rise of "shadow autonomy" presents a fundamental security challenge to the industry. While comprehensive controls for Agentic AI identities, Agentic AI applications, MCP, and RAG are discussed in the previous blogs, the core issue lies in determining the appropriate level of security for each agent type, rather than implementing every possible control everywhere. This is not a matter of convenience, but a critical security imperative. The foundational principle for a resilient AI system is to rigorously select a pattern that is commensurate with the agent’s complexity and the potential risk it introduces. These five patterns are the most widely used in agentic AI use cases, and identifying the right patterns or anti-patterns and controls is critical to adopting AI with necessary governance and security. 🟥 UNATTENDED SYSTEM AGENTS How It Works: Run without user consent, authenticated by system tokens. Risk: HIGH Use Cases: Background AI data processing, monitoring, data annotation, and event classification. Controls: ✅ Trusted event sources ✅ Read-only or data enrichment actions ✅ MTLS for strong auth ✅ Prompt injection guardrails Anti-Patterns: ❌ Access to untrusted inputs ❌ Arbitrary code/external calls 🟥 USER IMPERSONATION AGENTS How It Works: Act as a proxy with the user’s token (OAuth/JWT). Risk: HIGH Use Cases: Assistants retrieving knowledge, dashboards, low-risk workflows. Controls: ✅ Read-only or limited APIs ✅ Output guardrails ✅ MTLS Anti-Patterns: ❌ Write/state-changing ops ❌ Privileged APIs 🟨 ATTENDED SYSTEM AGENTS How It Works: Service identity with OAuth/API tokens, with human approval required. Risk: MEDIUM Use Cases: DevSec AI, privileged updates, infra changes. Controls: ✅ Explicit user approval ✅ Logging & audits ✅ MTLS Anti-Patterns: ❌ Blanket downstream access ❌ Unsafe ops (delete/shutdown) ❌ Unmanaged API escalation 🟩 USER DELEGATED AGENTS How It Works: OAuth 2.0 on-behalf-of token (OBO) exchange binds user + agent with consent and traceability. Risk: LOW Use Cases: Recommended for high-risk agent autonomy Controls: ✅ Time-bound consent ✅ Strict API scoping ✅ MTLS Anti-Patterns: ❌ Long-lived refresh tokens ❌ Write/state-changing ops 🟥 MULTI-AGENT SYSTEMS (MAS) How It Works: Multiple agents coordinate with dynamic identities. Hybrid + third-party. Risk: HIGH Use Cases: Decentralized AI with hybrid, in-house + vendor agents. Controls: ✅ Federated SSO ✅ MTLS for all comms ✅ Dynamic authorization ✅ Behavior monitoring ✅ MAS incident response Anti-Patterns: ❌ Static tokens ❌ No custody chain ❌ No secure framework ⚖️ BOTTOM LINE: Security controls must map to agent complexity and risk. From high-risk impersonation to low-risk delegated models with explicit consent and traceability, these patterns deliver proportionate controls, governance, and resilience in agentic AI adoption. #AgenticAI #AISecurity #ShadowAutonomy

𝐓𝐡𝐞 𝐅𝐢𝐫𝐬𝐭 𝐑𝐞𝐚𝐥 𝐏𝐥𝐚𝐲𝐛𝐨𝐨𝐤 𝐟𝐨𝐫 𝐒𝐞𝐜𝐮𝐫𝐢𝐧𝐠 𝐀𝐠𝐞𝐧𝐭𝐢𝐜 𝐀𝐈 Most AI security guidance today still focuses on models. But the real risk is shifting to agents. Agents don’t just answer questions. They plan, act, call APIs, write code, update memory, and trigger workflows. Which means they open 𝐞𝐧𝐭𝐢𝐫𝐞𝐥𝐲 𝐧𝐞𝐰 𝐚𝐭𝐭𝐚𝐜𝐤 𝐬𝐮𝐫𝐟𝐚𝐜𝐞𝐬: → Memory poisoning → Rogue agents in multi-agent swarms → Cascading hallucinations turning into system-wide failures → Tool misuse leading to RCE or database compromise → Communication poisoning between agents → Overwhelming human-in-the-loop reviewers (a new DoS vector) The 𝘖𝘞𝘈𝘚𝘗 𝘚𝘦𝘤𝘶𝘳𝘪𝘯𝘨 𝘈𝘨𝘦𝘯𝘵𝘪𝘤 𝘈𝘱𝘱𝘭𝘪𝘤𝘢𝘵𝘪𝘰𝘯𝘴 𝘎𝘶𝘪𝘥𝘦 makes one thing clear: 𝐀𝐠𝐞𝐧𝐭 𝐬𝐞𝐜𝐮𝐫𝐢𝐭𝐲 𝐢𝐬𝐧’𝐭 𝐚 𝐬𝐢𝐧𝐠𝐥𝐞 𝐜𝐨𝐧𝐭𝐫𝐨𝐥. 𝐈𝐭’𝐬 𝐚 𝐥𝐢𝐟𝐞𝐜𝐲𝐜𝐥𝐞 𝐝𝐢𝐬𝐜𝐢𝐩𝐥𝐢𝐧𝐞. Highlights every enterprise team should pay attention to: → 𝐀𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐚𝐥 𝐜𝐡𝐨𝐢𝐜𝐞 = 𝐬𝐞𝐜𝐮𝐫𝐢𝐭𝐲 𝐩𝐫𝐨𝐟𝐢𝐥𝐞. Sequential, hierarchical, and swarm patterns all introduce different risks. Orchestrators, in particular, are single points of failure and prime attack targets. → 𝐃𝐞𝐯𝐞𝐥𝐨𝐩𝐞𝐫 𝐩𝐫𝐚𝐜𝐭𝐢𝐜𝐞𝐬 𝐦𝐮𝐬𝐭 𝐜𝐡𝐚𝐧𝐠𝐞. Secure prompt engineering, HITL checkpoints, memory access policies, and I/O sanitization are now as important as code review. Expecting deterministic safety from probabilistic agents is a recipe for brittleness. → 𝐑𝐮𝐧𝐭𝐢𝐦𝐞 𝐬𝐞𝐜𝐮𝐫𝐢𝐭𝐲 𝐢𝐬 𝐧𝐨𝐧-𝐧𝐞𝐠𝐨𝐭𝐢𝐚𝐛𝐥𝐞. Continuous monitoring, anomaly detection, runtime guardrails, and tamper-proof agent identity should be standard. Without runtime defenses, cascading errors spread unchecked. → 𝐎𝐩𝐞𝐫𝐚𝐭𝐢𝐨𝐧𝐚𝐥 𝐫𝐢𝐠𝐨𝐫. Sandboxing, JIT credentials, red teaming, and incident response planning must evolve beyond model misuse to cover orchestration exploits, rogue sub-agents, and compromised workflows. This is OWASP’s first practical framework for agent builders. Your agents are only as secure as your weakest architectural decision. Download the guide. Implement these practices now. Before you become a cautionary tale. 🔔 Follow for commentary at the intersection of AI, technology leadership, and business outcomes.