Strategies for Mitigating Data Risks

I, too, want to share this precious work by my friend Isabel Barberá – a standout contribution to the field of privacy and AI. Her new report for the European Data Protection Board on privacy risks in LLMs has been making the rounds for good reason. Link to report: https://lnkd.in/gHmmiM-5 The report provides practical guidance for managing privacy risks in LLM-based systems. It covers data flows, risk identification and evaluation, mitigation strategies, and residual risk management. Real-world use cases and references to tools and standards make it a valuable resource for applying privacy-by-design across the AI lifecycle. I especially appreciate the section categorizing risks by LLM service model (pp. 26–43): - LLM as a Service (e.g., GPT-4 via API): Hosted models accessed externally. - Off-the-Shelf LLMs (e.g., LLaMA): Locally deployed, customizable models. - Self-Developed LLMs: Fully built and hosted in-house. - Agentic AI Systems: Dynamic tools that plan, reason, and act using APIs and function calls. The report then breaks down how responsibilities shift between provider vs. deployer (AI Act) and controller vs. processor (GDPR), with role-specific guidance (pp. 43–47). From pages 43–56, it dives into risk identification, emphasizing that privacy risks depend on context, purpose, data types, and deployment models. Risk assessment must be dynamic and ongoing and include tools like threat modeling and evidence-based analysis (e.g., logs, red teaming, user feedback). On pages 57-73 the report then offers a clear, structured process for risk estimation and evaluation, tailored for LLM systems. It introduces a sophisticated taxonomy-based scoring frameworks for both probability and severity. The next sections outline how to control, evaluate, and manage privacy risks in LLM systems through a comprehensive, lifecycle-based risk management process (p. 75-79). It walks through risk treatment options (mitigate, transfer, avoid, or accept), and gives detailed mitigation measures mapped to common LLM privacy risks, and emphasizes residual risk evaluation, continuous monitoring, use of risk registers, and incident response planning. The section also introduces iterative risk management, integrating tools like LLMOps and red teaming across stages from design to deployment. Very helpful graphics support this section (see below, and pages 78-79). All of the above then gets practically applied (p. 80-96). The report concludes with the especially valuable Section 10: a curated repository of metrics (e.g., WEAT, Demographic Parity), benchmarks (GLUE, MMLU, AIR-BENCH), guardrails (content filters, human-in-the-loop), privacy-preserving tools (Microsoft Presidio, dp-RAG), threat modeling methods (PLOT4ai, MITRE ATLAS) and links to EU guidance and standards in progress. Thank you, Isabel, for this outstanding work and such a clear and actionable roadmap! 👏 👏 👏

The EDPB recently published a report on AI Privacy Risks and Mitigations in LLMs.   This is one of the most practical and detailed resources I've seen from the EDPB, with extensive guidance for developers and deployers. The report walks through privacy risks associated with LLMs across the AI lifecycle, from data collection and training to deployment and retirement, and offers practical tips for identifying, measuring, and mitigating risks.   Here's a quick summary of some of the key mitigations mentioned in the report:   For providers: • Fine-tune LLMs on curated, high-quality datasets and limit the scope of model outputs to relevant and up-to-date information. • Use robust anonymisation techniques and automated tools to detect and remove personal data from training data. • Apply input filters and user warnings during deployment to discourage users from entering personal data, as well as automated detection methods to flag or anonymise sensitive input data before it is processed. • Clearly inform users about how their data will be processed through privacy policies, instructions, warning or disclaimers in the user interface. • Encrypt user inputs and outputs during transmission and storage to protect data from unauthorized access. • Protect against prompt injection and jailbreaking by validating inputs, monitoring LLMs for abnormal input behaviour, and limiting the amount of text a user can input. • Apply content filtering and human review processes to flag sensitive or inappropriate outputs. • Limit data logging and provide configurable options to deployers regarding log retention. • Offer easy-to-use opt-in/opt-out options for users whose feedback data might be used for retraining.   For deployers: • Enforce strong authentication to restrict access to the input interface and protect session data. • Mitigate adversarial attacks by adding a layer for input sanitization and filtering, monitoring and logging user queries to detect unusual patterns. • Work with providers to ensure they do not retain or misuse sensitive input data. • Guide users to avoid sharing unnecessary personal data through clear instructions, training and warnings. • Educate employees and end users on proper usage, including the appropriate use of outputs and phishing techniques that could trick individuals into revealing sensitive information. • Ensure employees and end users avoid overreliance on LLMs for critical or high-stakes decisions without verification, and ensure outputs are reviewed by humans before implementation or dissemination. • Securely store outputs and restrict access to authorised personnel and systems.   This is a rare example where the EDPB strikes a good balance between practical safeguards and legal expectations. Link to the report included in the comments.   #AIprivacy #LLMs #dataprotection #AIgovernance #EDPB #privacybydesign #GDPR

DIB: The DoD’s Implementation Plan Brings CMMC Level 3 Requirements Before Phase 4 (Full Implementation). While much of the focus has been on CMMC Level 2, it’s equally important to prepare for the significant lift required for Level 3. The transition to L3 will depend on your existing CUI Program, leadership support, and your technical team’s skill set. Key elements to consider: 1. Access Control for only organization-owned/managed devices, no Personal devices (BYOD). Also, apply Golden Images to Level 3 assets, ensuring consistency and security, followed by conditional access controls or systems posture checks. 2. Must protect the integrity of Secure Baseline Configuration/Golden Images. 3. Encryption In Transit and At Rest with Transport Layer Security (TLS), IEEE 802.1X, or IPsec. 4. Bidirectional/Mutual Authentication technology that ensures both parties in a communication session authenticate each other (see encryption). 5. Conduct L3-specific End-User Training, including practical training for end-users, power users, and administrators on phishing, social engineering, and cyber threats and test readiness and response. 6. Continuous Monitoring (ConMon), Automation, and Alerting to remove non-compliant systems promptly. 7. Automated Asset Discovery & Inventory, ensuring full visibility of all assets. 8. Security Operations Center (SOC) and Incident Response (IR): Maintain a 24x7 SOC and IR team to handle security incidents promptly and efficiently. 9. HR Response Plans that include Blackmail Resilience to address scenarios like blackmail, insider threats, and other HR-related security issues. 10. Mandatory Threat Hunting to proactively identify and mitigate threats. 11. Automated Risk Identification and Analytics using Security Information and Event Management (SIEM), Security Orchestration, Automation, and Response (SOAR), Extended Detection and Response (XDR), etc. 12. Risk-Informed Security Control Selection to ensure tailored and effective protection measures. 13. Supply Chain Risk Management (SCRM), Monitoring & Testing of Service Provider Agreements (SPAs): Regularly monitor and test SPAs to ensure compliance with security requirements and to mitigate risks associated with third-party vendors and suppliers. 14. Mandatory Penetration Testing to identify and rectify system vulnerabilities. 15. Secure Management of Operational Technology (OT)/Industrial Control Systems (ICS), including Government-Furnished Equipment (GFE) and other critical infrastructure. 16. Root and Trust Mechanisms to verify the authenticity and integrity of software. Ensure devices boot using only trusted software. Provide hardware-based security functions such as TPM. 17. Threat Intelligence and Indicator of Compromise (IOC) Monitoring to stay ahead of emerging threats and quickly respond. #CUI #hva #ProtectCUI

Everyone’s feeding data into AI engines, but when it leaves secure systems, the guardrails are often gone. Exposure grows, controls can break down, and without good data governance, your organization's most important assets may be at risk. Here's what needs to happen: 1. Have an established set of rules about what’s allowed/not allowed regarding the use of organizational data that is shared organization-wide, not just with the IT organization and the CISO team. 2. Examine the established controls on information from origin to destination and who has access every step of the way: end users, system administrators, and other technology support people. Implement new controls where needed to ensure the proper handling and protection of critical data. You can have great technical controls, but if there are way too many people who have access and who don’t need it for legitimate business or mission purposes, it puts your organization at risk. 3. Keep track of the metadata that is collected and how well it’s protected. Context matters. There’s a whole ecosystem associated with any network activity or data interchange, from emails or audio recordings to bank transfers. There’s the transaction itself and its contents, and then there’s the metadata about the transaction and the systems and networks that it traversed on its way from point A to point B. This metadata can be used by adversaries to engineer successful cyberattacks. 4. Prioritize what must be protected In every business, some data has to be more closely managed than others. At The Walt Disney Company, for example, we heavily protected the dailies (the output of the filming that went on that day) because the IP was worth millions. In government, it was things like planned military operations that needed to be highly guarded. You need an approach that doesn’t put mission-critical protections on what the cafeteria is serving for lunch, or conversely, let a highly valuable transaction go through without a VPN, encryption, and other protections that make it less visible. Takeaway: Data is a precious commodity and one of the most valuable assets an organization can have today. Because the exchange-for-value is potentially so high, bad actors can hold organizations hostage and demand payment simply by threatening to use it.

🚀 Debbie Reynolds, "The Data Diva" and The Data Privacy Advantage Newsletter present "The Data Privacy Vector of Business Risk - Navigating the Emerging Data Risk Frontier for Organizations"🚀 🔐 "Privacy is a data problem with legal implications, not a legal problem with data implications." - Debbie Reynolds, "The Data Diva"🔐 📉Many organizations traditionally viewed privacy as a regulatory and legal issue. However, with rising data breaches, lack of transparency in data handling, and the growing adoption of emerging technologies, a new Data Privacy Vector of Business Risk has emerged. 📉 🛡️ What is the Data Privacy Vector of Business Risk? It's created when data problems escalate, leading to increased risks as data is collected, duplicated, and used throughout an organization. These risks can be mitigated by focusing on data issues before they become legal problems. Here are three strategies: 🛡️Data Risk Prevention Purpose Tracking: Ensure data's purpose travels with it throughout its lifecycle High-Risk Use Case Monitoring: Identify and mitigate high-risk data usage scenarios Regular Audits and Assessments: Implement audits to identify and address data risks 🛡️Data Curation Understanding Proper Data Uses: Ensure data usage aligns with its intended purpose Minimizing Data Redundancy: Avoid unnecessary data duplication Data Stewardship: Assign stewards to manage data assets and ensure compliance 🛡️Data Lifecycle Sunsetting Data Retention Policies: Establish clear policies for data retention based on regulatory and business needs Regular Data Deletion: Promptly delete data no longer needed Data Anonymization: Protect individual privacy by anonymizing data 🌟 By prioritizing these strategies, organizations can: Ensure robust data governance Prevent data misuse Maintain data integrity and compliance Minimize privacy risks Embrace these strategies to safeguard individual privacy and fortify your business against evolving data challenges. Let's make Data Privacy a Business Advantage! 💼 #privacy #cybersecurity #datadiva #DataPrivacy #BusinessRisk #DataGovernance #EmergingTechnologies #PrivacyByDesign