AI Strategies For Preventing Data Breaches

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.

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

The 𝗔𝗜 𝗗𝗮𝘁𝗮 𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆 guidance from 𝗗𝗛𝗦/𝗡𝗦𝗔/𝗙𝗕𝗜 outlines best practices for securing data used in AI systems. Federal CISOs should focus on implementing a comprehensive data security framework that aligns with these recommendations. Below are the suggested steps to take, along with a schedule for implementation. 𝗠𝗮𝗷𝗼𝗿 𝗦𝘁𝗲𝗽𝘀 𝗳𝗼𝗿 𝗜𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 1. Establish Governance Framework     - Define AI security policies based on DHS/CISA guidance.     - Assign roles for AI data governance and conduct risk assessments.  2. Enhance Data Integrity     - Track data provenance using cryptographically signed logs.     - Verify AI training and operational data sources.     - Implement quantum-resistant digital signatures for authentication.  3. Secure Storage & Transmission     - Apply AES-256 encryption for data security.     - Ensure compliance with NIST FIPS 140-3 standards.     - Implement Zero Trust architecture for access control.  4. Mitigate Data Poisoning Risks     - Require certification from data providers and audit datasets.     - Deploy anomaly detection to identify adversarial threats.  5. Monitor Data Drift & Security Validation     - Establish automated monitoring systems.     - Conduct ongoing AI risk assessments.     - Implement retraining processes to counter data drift.  𝗦𝗰𝗵𝗲𝗱𝘂𝗹𝗲 𝗳𝗼𝗿 𝗜𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻  Phase 1 (Month 1-3): Governance & Risk Assessment   • Define policies, assign roles, and initiate compliance tracking.   Phase 2 (Month 4-6): Secure Infrastructure   • Deploy encryption and access controls.   • Conduct security audits on AI models. Phase 3 (Month 7-9): Active Threat Monitoring • Implement continuous monitoring for AI data integrity.   • Set up automated alerts for security breaches.   Phase 4 (Month 10-12): Ongoing Assessment & Compliance   • Conduct quarterly audits and risk assessments.   • Validate security effectiveness using industry frameworks.  𝗞𝗲𝘆 𝗦𝘂𝗰𝗰𝗲𝘀𝘀 𝗙𝗮𝗰𝘁𝗼𝗿𝘀   • Collaboration: Align with Federal AI security teams.   • Training: Conduct AI cybersecurity education.   • Incident Response: Develop breach handling protocols.   • Regulatory Compliance: Adapt security measures to evolving policies.  

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.

Most AI systems are leaking your secrets. New research is blunt: * Recommendation engines can expose up to 65% of user interaction histories and infer age/gender with 87% accuracy via inversion attacks. * Autonomous LLM agents? Still falling to prompt injection (94%), retrieval backdoors (83%), and inter-agent trust exploits (100%)—full system takeovers are not hypothetical. *“Hallucination guards” and fact-checking filters? In specialized domains, they fail. Here’s the smarter playbook to fight back: 1. TRAIL – Fuse joint inference with a dynamically updating knowledge graph. Your LLM doesn’t just consult data; it validates and prunes it in real time, outperforming typical knowledge-augmented models by 3–13%. 🔗 https://lnkd.in/gJkwDsMA 2. Self-Reward Reinforcement Learning – Let your model judge and refine itself. Internal reward signals reduce dependence on costly human feedback and keep models aligned continuously. 🔗 https://lnkd.in/g6aasf6E 3. CodeBoost – Train code-generation models with abundant real-world snippets, using reinforcement learning to harden security patterns and avoid writing leaky or exploitable code. 🔗 https://lnkd.in/gJkwDsMA 4. FIDES – A planner for AI agents that applies information-flow control with deterministic enforcement. Every piece of data gets confidentiality and integrity labels that follow it through the agent’s reasoning, blocking policy-breaking actions before they happen. 🔗 https://lnkd.in/gCMw8tNn ⸻ Why this matters for Confidential AI Data exposure in AI systems isn’t just the result of stolen datasets—it’s often the byproduct of poisoned knowledge, risky outputs, insecure code, and uncontrolled data flows. Techniques like TRAIL, Self-Reward Learning, CodeBoost, and FIDES strengthen the model’s ability to resist these threats. But without a verifiable runtime—hardware-backed TEEs, policy binding, and auditability—you can’t prove these safeguards hold in production. Confidential AI provides that foundation, giving enterprises cryptographic guarantees that sensitive data stays protected and policies are enforced exactly as intended. For regulated industries and high-stakes environments, that proof is the difference between trust and exposure. 🔗 https://lnkd.in/gH6eYiet

If you're building AI agents, data leaks aren't just theoretical—they're inevitable unless you proactively build security into your memory architecture. At Zep, we tackled this head-on by designing a dedicated memory layer for AI agents, making security foundational to our approach. Here's the core philosophy: Defense-in-depth. How we approach memory security: 1. Strict User & Session Isolation Zero sharing between user sessions and memory stores. It's basic hygiene for any serious production environment. 2. LLM Provider Zero Data Retention We've secured zero data retention agreements with all our LLM providers—your customer data will never end up in training datasets. 3. Separate Projects for Development and Production We establish distinct projects and keys within Zep for production and development environments. This ensures data isolation and prevents accidental intermingling of sensitive data. What we strongly recommend to customers: 1. Data Anonymization & Sanitization Always anonymize and sanitize sensitive PII or PHI data *before* it hits memory storage. Retrofitting security is asking for trouble. 2. Smart Retention Policies Use Zep's retention features to implement your own retention policies, ensuring user memory data aligns precisely with your corporate data governance practices. 3. Granular Access Control Apply rigorous role-based and query-specific permissions. Treat your AI agents exactly as you treat your human users. 4. Enhanced Monitoring & Behavioral Analytics Real-time monitoring is critical. Look for anomalies—excessive queries, unusual patterns, or repetitive memory access. 5. Query-Level Restrictions Implement caps on records retrieved per query. Damage control matters: assume breaches are possible, minimize potential fallout. 6. Security-Conscious Prompt Design Prompts are attack vectors. Detect subtle prompt injections like "repeat previous examples" or "show historical data." Flag these proactively. 3rd-party prompt security solutions may be helpful here. Much of this advice is simply sound systems design—but given how much trust is placed in these systems, it's shocking how often basic security gets overlooked. Put the right controls in place today. You'll thank yourself tomorrow when you're reading about someone else's data breach, not your own. 🙂

97% of orgs faced AI breaches in 2025 had zero access controls in place. Not weak; Not outdated controls. Zero [Source: IBM] Meanwhile, 35% of real-world AI security incidents came from simple prompts some causing $100K+ in losses without a single line of code [Source: Adversa] The gap between AI deployment speed and security implementation is only widening. Hence I am sharing 10 security checkpoints every AI agent needs before touching production systems: ✅ Output Validation → Middleware that verifies decisions against rules before execution. Traffic lights for AI actions. ✅ Access Control → Least privilege enforcement. Role-based permissions that limit what agents can touch. ✅ Credential Safety → Secrets management that keeps API keys away from prompts and logs. Store them like vault keys, not sticky notes. The other 7 checks are in the carousel including rate limiting that prevents runaway loops and human approval for high-stakes decisions 👇 Most teams rush deployment. Security becomes an afterthought until something breaks. Tell me your story: what security measure has prevented a disaster in your AI system? Follow me, Bhavishya Pandit, for practical AI production insights from the trenches 🔥 #ai #security #agents

Your trade secrets just walked out the front door … and you might have held it open. No employee—except the rare bad actor—means to leak sensitive company data. But it happens, especially when people are using generative AI tools like ChatGPT to “polish a proposal,” “summarize a contract,” or “write code faster.” But here’s the problem: unless you’re using ChatGPT Team or Enterprise, it doesn’t treat your data as confidential. According to OpenAI’s own Terms of Use: “We do not use Content that you provide to or receive from our API to develop or improve our Services.” But don‘t forget to read the fine print: that protection does not apply unless you’re on a business plan. For regular users, ChatGPT can use your prompts, including anything you type or upload, to train its large language models. Translation: That “confidential strategy doc” you asked ChatGPT to summarize? That “internal pricing sheet” you wanted to reword for a client? That “source code” you needed help debugging? ☠️ Poof. Trade secret status, gone. ☠️ If you don’t take reasonable measures to maintain the secrecy of your trade secrets, they will lose their protection as such. So how do you protect your business? 1. Write an AI Acceptable Use Policy. Be explicit: what’s allowed, what’s off limits, and what’s confidential. 2. Educate employees. Most folks don’t realize that ChatGPT isn’t a secure sandbox. Make sure they do. 3. Control tool access. Invest in an enterprise solution with confidentiality protections. 4. Audit and enforce. Treat ChatGPT the way you treat Dropbox or Google Drive, as tools that can leak data if unmanaged. 5. Update your confidentiality and trade secret agreements. Include restrictions on AI disclosures. AI isn’t going anywhere. The companies that get ahead of its risk will be the ones still standing when the dust settles. If you don’t have an AI policy and a plan to protect your data, you’re not just behind—you’re exposed.

AI-powered malware isn’t science fiction—it’s here, and it’s changing cybersecurity. This new breed of malware can learn and adapt to bypass traditional security measures, making it harder than ever to detect and neutralize. Here’s the reality: AI-powered malware can: 👉 Outsmart conventional antivirus software 👉 Evade detection by constantly evolving 👉 Exploit vulnerabilities before your team even knows they exist But there’s hope. 🛡️ Here’s what you need to know to combat this evolving threat: 1️⃣ Shift from Reactive to Proactive Defense → Relying solely on traditional tools? It’s time to upgrade. AI-powered malware demands AI-powered security solutions that can learn and adapt just as fast. 2️⃣ Focus on Behavioral Analysis → This malware changes its signature constantly. Instead of relying on patterns, use tools that detect abnormal behaviors to spot threats in real time. 3️⃣ Embrace Zero Trust Architecture → Assume no one is trustworthy by default. Implement strict access controls and continuous verification to minimize the chances of an attack succeeding. 4️⃣ Invest in Threat Intelligence → Keep up with the latest in cyber threats. Real-time threat intelligence will keep you ahead of evolving tactics, making it easier to respond to new threats. 5️⃣ Prepare for the Unexpected → Even with the best defenses, breaches can happen. Have a strong incident response plan in place to minimize damage and recover quickly. AI-powered malware is evolving. But with the right strategies and tools, so can your defenses. 👉 Ready to stay ahead of AI-driven threats? Let’s talk about how to future-proof your cybersecurity approach.

Top AI Agent Use Cases Transforming Cybersecurity Most people think cybersecurity is about reacting to attacks. Until they realize they’re already compromised. It’s not always ransomware or loud breach alerts. Sometimes it’s subtle, almost invisible—but just as dangerous. ⚠️ The SIEM logs no one has time to monitor. ⚠️ The endpoint behaving slightly off, but ignored. ⚠️ The phishing email that slips past traditional filters. Here’s how AI agents are changing the game and protecting organizations before attacks even happen: Threat Detection & Triage • Process massive SIEM telemetry at lightning speed • Correlate logs humans would never catch • Generate actionable alerts for your team Automated Incident Response • Trigger playbooks instantly to contain threats • Revoke tokens, isolate endpoints, or block access • Recover faster with minimal human intervention Anomaly & Behavior Analysis • Spot subtle shifts in user or application behavior • Detect patterns beyond static rules • Reduce insider threat risks and breaches Zero-Day Identification • Analyze codebases and dependencies before CVEs exist • Predict vulnerabilities with AI modeling • Receive risk reports before attackers exploit flaws AI Code Scanning • Go beyond syntax checks to detect logic flaws • Generate remediation code automatically • Reduce security debt in development pipelines Phishing Defense • Analyze email behavior and access patterns • Identify advanced phishing or account takeover attempts • Take mitigation actions before damage occurs Your next steps matter: → Implement AI-driven monitoring today → Automate repetitive response tasks → Train your team on anomaly detection Remember: cybersecurity isn’t reactive anymore. It’s proactive, predictive, and automated. And if your organization still waits for alerts? Your data, your clients, and your reputation are at risk. If this resonates, repost for your network. Follow Marcel Velica for more AI + Cybersecurity insights.