Data Security Issues in Artificial Intelligence

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

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?

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?

🚨 𝐓𝐡𝐞 𝐇𝐢𝐝𝐝𝐞𝐧 𝐓𝐡𝐫𝐞𝐚𝐭𝐬 𝐭𝐨 𝐀𝐈 𝐒𝐞𝐜𝐮𝐫𝐢𝐭𝐲: 𝐖𝐡𝐚𝐭 𝐘𝐨𝐮 𝐍𝐞𝐞𝐝 𝐭𝐨 𝐊𝐧𝐨𝐰 🚨 Imagine your AI system making decisions based on data that's been subtly tampered with. Sounds like science fiction? Think again. Security researcher 𝐽𝑜ℎ𝑎𝑛𝑛 𝑅𝑒ℎ𝑏𝑒𝑟𝑔𝑒𝑟 recently uncovered vulnerabilities in AI models like ChatGPT that could allow malicious actors to inject harmful instructions and extract sensitive data over time. As AI becomes integral to our decision-making processes, we have to ask: 𝐇𝐨𝐰 𝐬𝐞𝐜𝐮𝐫𝐞 𝐚𝐫𝐞 𝐭𝐡𝐞𝐬𝐞 𝐬𝐲𝐬𝐭𝐞𝐦𝐬, 𝐚𝐧𝐝 𝐰𝐡𝐚𝐭 𝐬𝐭𝐞𝐩𝐬 𝐜𝐚𝐧 𝐰𝐞 𝐭𝐚𝐤𝐞 𝐭𝐨 𝐩𝐫𝐨𝐭𝐞𝐜𝐭 𝐭𝐡𝐞𝐦? 🔍 𝐓𝐡𝐞 𝐂𝐮𝐫𝐫𝐞𝐧𝐭 𝐋𝐚𝐧𝐝𝐬𝐜𝐚𝐩𝐞: 🛑 𝐃𝐚𝐭𝐚 𝐌𝐚𝐧𝐢𝐩𝐮𝐥𝐚𝐭𝐢𝐨𝐧 𝐑𝐢𝐬𝐤𝐬: AI models are susceptible to adversarial inputs- malicious data crafted to deceive or influence system outputs. 🕵️♂️ 𝐒𝐢𝐥𝐞𝐧𝐭 𝐄𝐱𝐩𝐥𝐨𝐢𝐭𝐚𝐭𝐢𝐨𝐧: Attackers might manipulate AI behavior or siphon off confidential information without immediate detection. 🔒 𝐁𝐞𝐲𝐨𝐧𝐝 𝐓𝐫𝐚𝐝𝐢𝐭𝐢𝐨𝐧𝐚𝐥 𝐒𝐞𝐜𝐮𝐫𝐢𝐭𝐲: Firewalls and standard cybersecurity measures aren't enough. We need strategies that ensure AI systems process and learn from trustworthy data. 🤔 𝐏𝐨𝐢𝐧𝐭𝐬 𝐭𝐨 𝐂𝐨𝐧𝐬𝐢𝐝𝐞𝐫: 🔓 𝐓𝐫𝐚𝐧𝐬𝐩𝐚𝐫𝐞𝐧𝐜𝐲 𝐯𝐬. 𝐒𝐞𝐜𝐮𝐫𝐢𝐭𝐲: How do we balance the openness that fosters AI innovation with the need to protect against exploitation? 🤝 𝐂𝐨𝐥𝐥𝐞𝐜𝐭𝐢𝐯𝐞 𝐑𝐞𝐬𝐩𝐨𝐧𝐬𝐢𝐛𝐢𝐥𝐢𝐭𝐲: What roles do developers, organizations, and users play in safeguarding AI systems? 🚀 𝐅𝐮𝐭𝐮𝐫𝐞 𝐈𝐦𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬: If AI can be manipulated today, what does this mean for more advanced systems tomorrow? 🔑 𝐖𝐡𝐚𝐭 𝐂𝐚𝐧 𝐖𝐞 𝐃𝐨? 📖 𝐒𝐭𝐚𝐲 𝐈𝐧𝐟𝐨𝐫𝐦𝐞𝐝: Keep abreast of the latest developments in AI security to understand potential vulnerabilities. 🛠️ 𝐏𝐫𝐨𝐦𝐨𝐭𝐞 𝐁𝐞𝐬𝐭 𝐏𝐫𝐚𝐜𝐭𝐢𝐜𝐞𝐬: Encourage the adoption of secure coding practices and regular audits in AI development. 🤝 𝐂𝐨𝐥𝐥𝐚𝐛𝐨𝐫𝐚𝐭𝐞 𝐨𝐧 𝐒𝐨𝐥𝐮𝐭𝐢𝐨𝐧𝐬: Work with industry peers, cybersecurity experts, and policymakers to develop robust defense mechanisms. In a world where AI influences everything from business strategies to personal recommendations, ensuring the integrity of these systems is paramount. 𝐂𝐚𝐧 𝐰𝐞 𝐚𝐟𝐟𝐨𝐫𝐝 𝐭𝐨 𝐨𝐯𝐞𝐫𝐥𝐨𝐨𝐤 𝐭𝐡𝐞 𝐬𝐞𝐜𝐮𝐫𝐢𝐭𝐲 𝐨𝐟 𝐭𝐡𝐞 𝐯𝐞𝐫𝐲 𝐭𝐨𝐨𝐥𝐬 𝐬𝐡𝐚𝐩𝐢𝐧𝐠 𝐨𝐮𝐫 𝐟𝐮𝐭𝐮𝐫𝐞? 💬 𝐋𝐞𝐭'𝐬 𝐬𝐭𝐚𝐫𝐭 𝐚 𝐜𝐨𝐧𝐯𝐞𝐫𝐬𝐚𝐭𝐢𝐨𝐧! What measures do you believe are essential in securing AI against emerging threats? Share your thoughts below! 🔽 🔗 Link to Johann Rehberger's analysis: https://lnkd.in/d9QVwE_5 #AI #Cybersecurity #DataIntegrity #FutureTech #Collaboration #AIEthics ¦ Deloitte

🔥 AI Security: The New Frontier of Patient Safety Cybersecurity used to mean protecting devices, networks, and data. In the age of AI, that is no longer enough. The new threat surface is the model itself. AI security now includes: • Model poisoning • Adversarial prompts • Data injection attacks • Synthetic identity creation • Algorithmic manipulation • Compromised training datasets • Unauthorized model extraction • Real-time clinical guidance distortion If your AI is compromised, your patient care is compromised. It’s that simple. Forward-looking healthcare leaders are pivoting from: “Protect the system” → to → “Protect the intelligence behind the system.” What we protect must now include: ✔️ Model integrity ✔️ Training data lineage ✔️ API security ✔️ Prompt security ✔️ Real-time monitoring of drift ✔️ Audit trails for algorithmic decisions ✔️ Red-team testing for AI vulnerabilities In 2026, AI security will become the new patient safety. Leaders who don’t understand AI risk cannot ensure clinical safety. — Khalid Turk MBA, PMP, CHCIO, FCHIME Building systems that work, teams that thrive, and cultures that endure.

How do you know your AI Agent is secure - When We Don’t Fully Understand How GenAI works? It’s no secret: even experts admit we don’t entirely understand how deep learning—at the heart of Generative AI—actually works. This unpredictability becomes a major challenge when security is on the line. Just last week, Microsoft confirmed a critical security flaw in 365 Copilot, the AI embedded into its Office suite. The vulnerability was discovered in January—but wasn’t resolved until five months later. Why the delay? According to experts, it’s because GenAI systems are notoriously difficult to lock down, given their vast and unpredictable attack surfaces. What’s especially concerning is the nature of this vulnerability. Unlike traditional cyber attacks, where a user is tricked into clicking a malicious link, AI tools like Copilot can be manipulated directly—even without any user error. Sensitive files could be exposed simply because the model was misled. The team at Aim Security, which uncovered the flaw, warns that this issue could extend beyond Microsoft to any AI system that integrates with third-party tools—like Anthropic’s MCP or Salesforce’s Agentforce. The root issue? AI models don’t currently differentiate well between trusted instructions and untrusted data. It’s like asking someone to follow every instruction they read—regardless of the source. Fixing this may require more than just patches—it could call for a fundamental redesign of how AI agents are built. That might mean: • New model architectures that distinguish clearly between instruction and data. • Stronger guardrails at the application level. • Or a combination of both. As GenAI becomes more embedded in the tools we use every day, it’s time we ask: Are we building smart enough to stay safe? Source: https://lnkd.in/eP8YZaCm

I'm increasingly convinced that we need to treat "AI privacy" as a distinct field within privacy, separate from but closely related to "data privacy". Just as the digital age required the evolution of data protection laws, AI introduces new risks that challenge existing frameworks, forcing us to rethink how personal data is ingested and embedded into AI systems. Key issues include: 🔹 Mass-scale ingestion – AI models are often trained on huge datasets scraped from online sources, including publicly available and proprietary information, without individuals' consent. 🔹 Personal data embedding – Unlike traditional databases, AI models compress, encode, and entrench personal data within their training, blurring the lines between the data and the model. 🔹 Data exfiltration & exposure – AI models can inadvertently retain and expose sensitive personal data through overfitting, prompt injection attacks, or adversarial exploits. 🔹 Superinference – AI uncovers hidden patterns and makes powerful predictions about our preferences, behaviours, emotions, and opinions, often revealing insights that we ourselves may not even be aware of. 🔹 AI impersonation – Deepfake and generative AI technologies enable identity fraud, social engineering attacks, and unauthorized use of biometric data. 🔹 Autonomy & control – AI may be used to make or influence critical decisions in domains such as hiring, lending, and healthcare, raising fundamental concerns about autonomy and contestability. 🔹 Bias & fairness – AI can amplify biases present in training data, leading to discriminatory outcomes in areas such as employment, financial services, and law enforcement. To date, privacy discussions have focused on data - how it's collected, used, and stored. But AI challenges this paradigm. Data is no longer static. It is abstracted, transformed, and embedded into models in ways that challenge conventional privacy protections. If "AI privacy" is about more than just the data, should privacy rights extend beyond inputs and outputs to the models themselves? If a model learns from us, should we have rights over it? #AI #AIPrivacy #Dataprivacy #Dataprotection #AIrights #Digitalrights

The Cybersecurity and Infrastructure Security Agency together with the National Security Agency, the Federal Bureau of Investigation (FBI), the National Cyber Security Centre, and other international organizations, published this advisory providing recommendations for organizations in how to protect the integrity, confidentiality, and availability of the data used to train and operate #artificialintelligence. The advisory focuses on three main risk areas: 1. Data #supplychain threats: Including compromised third-party data, poisoning of datasets, and lack of provenance verification. 2. Maliciously modified data: Covering adversarial #machinelearning, statistical bias, metadata manipulation, and unauthorized duplication. 3. Data drift: The gradual degradation of model performance due to changes in real-world data inputs over time. The best practices recommended include: - Tracking data provenance and applying cryptographic controls such as digital signatures and secure hashes. - Encrypting data at rest, in transit, and during processing—especially sensitive or mission-critical information. - Implementing strict access controls and classification protocols based on data sensitivity. - Applying privacy-preserving techniques such as data masking, differential #privacy, and federated learning. - Regularly auditing datasets and metadata, conducting anomaly detection, and mitigating statistical bias. - Securely deleting obsolete data and continuously assessing #datasecurity risks. This is a helpful roadmap for any organization deploying #AI, especially those working with limited internal resources or relying on third-party data.

I recently co-authored an article with Sylvain Chambon, Principal Solutions Architect at MongoDB, exploring hidden security risks in Generative AI systems across four critical zones. 🔐 Zone 1: Input and Output Manipulation • Vulnerabilities: Prompt injection attacks and insecure output handling can manipulate AI behavior and expose systems to threats. • Mitigation: Implement input validation, use immutable system prompts, and sanitize AI outputs. 🔐 Zone 2: Data Security and Privacy Risks • Vulnerability: AI unintentionally revealing sensitive information learned during training. • Mitigation: Apply data segmentation, enforce role-based access control (RBAC), use data encryption, and monitor systems regularly. 🔐 Zone 3: Resource Exploitation and Denial of Service • Vulnerability: Denial of Service (DoS) attacks can overwhelm AI resources. • Mitigation: Implement rate limiting, restrict input sizes, and utilize auto-scaling infrastructure. 🔐 Zone 4: Access and Privilege Control • Vulnerabilities: Excessive agency and insecure plugin designs can grant undue access or control. • Mitigation: Enforce strict RBAC, validate all plugins and tools, and secure the supply chain. While we’ve highlighted these areas, I acknowledge there’s always more to learn, and our solutions might not cover every scenario. I welcome any feedback or critical thoughts you might have. 👉 Read the full article here: https://lnkd.in/g7jW7Wcr Looking forward to a constructive dialogue to enhance AI security together! Jack Fischer Gregory Maxson Henry Weller Richmond Alake Gabriel Paranthoen David Alker Pierre P. Emil Nildersen Brice Saccucci

Let's make it clear: We need more frameworks for evaluating data protection risks in AI systems. As I delve into this topic, more and more new papers and risk assessment approaches appear. One of them is described in the paper titled "Rethinking Data Protection in the (Generative) Artificial Intelligence Era." 👉 My key takeaways: 1️⃣ Begin by identifying the data that should be protected in AI systems. Authors recommend focusing on the following: •  Training Datasets •  Trained Models •  Deployment-integrated Data (e.g., protect your internal system prompts and external knowledge bases like RAG). ❗ I loved this differentiation and risk assessment, as if, for example, an adversary discovers your system prompts, they might try to exploit them. Also, protecting sensitive RAG data is essential. •  User prompts (e.g., besides prompts protection, add transparency and let users know if prompts will be logged or used for training). •  AI-generated Content (e.g., ensure traceability to understand its provenance if used for training, etc.). 2️⃣ Authors also introduce an interesting taxonomy of data protection areas to focus on when dealing with generative AI: •  Level 1: Data Non-usability. Ensures that specified data cannot contribute to model learning or predicting in any way by using strategies that block any unauthorized party from using or even accessing protected data (e.g., encryption, access controls, unlearnable examples, non-transferable learning, etc.) •  Level 2: Data Privacy-preservation. Here, the focus is on how the training can be performed with enhanced privacy techniques (PETs): K-anonymity and L-diversity schemes, differential privacy, homomorphic encryption, federated learning, and split learning. •  Level 3: Data Traceability. This is about the ability to track the origin, history, and influence of data as it is used in AI applications during training and inference. This capability allows stakeholders to audit and verify data usage. This can be categorised into intrusive (e.g., digital watermarking with signatures to datasets, model parameters, or prompts) and non-intrusive methods (e.g., membership inference, model fingerprinting, cryptographic hashing, etc.). •  Level 4: Data Deletability. This is about the capacity to completely remove a specific piece of data and its influence from a trained model (authors recommend exploring unlearning techniques that specifically focus on erasing the influence of the data in the model, rather than the content or model itself). ------------------------------------------------------------------------ 👋 I'm Vadym, an expert in integrating privacy requirements into AI-driven data processing operations. 🔔 Follow me to stay ahead of the latest trends and to receive actionable guidance on the intersection of AI and privacy. ✍ Expect content that is solely authored by me, reflecting my reading and experiences. #AI #privacy #GDPR