Protecting Sensitive Data in Knowledge Management Systems

For companies that have strict data locality and compliance requirements, the ability to secure PII during data replication is crucial. A few ways that companies can handle PII effectively when it comes to data replication: 1️⃣ Column Exclusion: safeguard sensitive information by excluding specific columns from replication entirely, ensuring that they do not appear in the data warehouse or lake for downstream consumption. 2️⃣ Column Allowlist: utilize an allowlist to ensure only non-sensitive, pre-approved columns are replicated, minimizing the risk of exposing sensitive data. 3️⃣ Column Hashing: obfuscating sensitive PII into a hashed format, maintaining privacy while allowing for activity tracking and data analysis without actual data exposure. 4️⃣ Column Encryption: encrypt PII before replication to ensure that data is secure both in transit and at rest, accessible only via decryption keys. 5️⃣ Audit Trails: implement comprehensive logging to track changes to replicated data, which is essential for monitoring, compliance, and security investigations. 6️⃣ Geofencing: control data replication based on geographic boundaries to comply with laws like GDPR, which restricts cross-border data transfers. By integrating these strategies, companies can comply with strict data protection regulations and enhance their reputation by demonstrating a commitment to data security. 🔒 One of our customers is a B2C fintech platform. They use Artie (YC S23) to replicate customer and transaction data across platforms to analyze and monitor changes in risk scores. To ensure compliance with financial regulations and safeguard customer data, the company uses column hashing for sensitive financial details and customer identifiers. This way, they are able to identify important PII changes without exposing sensitive data to their analysts. Additionally, they implemented audit trails (our history mode/SCD tables!) to monitor and log all data changes. Geofencing is utilized to restrict data processing to specific regions, to remain compliant with regulations like GDPR. How is your organization managing PII in data replication? Are there other strategies you find effective? #dataengineering #datareplication #data

How To Handle Sensitive Information in your next AI Project It's crucial to handle sensitive user information with care. Whether it's personal data, financial details, or health information, understanding how to protect and manage it is essential to maintain trust and comply with privacy regulations. Here are 5 best practices to follow: 1. Identify and Classify Sensitive Data Start by identifying the types of sensitive data your application handles, such as personally identifiable information (PII), sensitive personal information (SPI), and confidential data. Understand the specific legal requirements and privacy regulations that apply, such as GDPR or the California Consumer Privacy Act. 2. Minimize Data Exposure Only share the necessary information with AI endpoints. For PII, such as names, addresses, or social security numbers, consider redacting this information before making API calls, especially if the data could be linked to sensitive applications, like healthcare or financial services. 3. Avoid Sharing Highly Sensitive Information Never pass sensitive personal information, such as credit card numbers, passwords, or bank account details, through AI endpoints. Instead, use secure, dedicated channels for handling and processing such data to avoid unintended exposure or misuse. 4. Implement Data Anonymization When dealing with confidential information, like health conditions or legal matters, ensure that the data cannot be traced back to an individual. Anonymize the data before using it with AI services to maintain user privacy and comply with legal standards. 5. Regularly Review and Update Privacy Practices Data privacy is a dynamic field with evolving laws and best practices. To ensure continued compliance and protection of user data, regularly review your data handling processes, stay updated on relevant regulations, and adjust your practices as needed. Remember, safeguarding sensitive information is not just about compliance — it's about earning and keeping the trust of your users.

Safeguarding information while enabling collaboration requires methods that respect privacy, ensure accuracy, and sustain trust. Privacy-Enhancing Technologies create conditions where data becomes useful without being exposed, aligning innovation with responsibility. When companies exchange sensitive information, the tension between insight and confidentiality becomes evident. Cryptographic PETs apply advanced encryption that allows data to be analyzed securely, while distributed approaches such as federated learning ensure that knowledge can be shared without revealing raw information. The practical benefits are visible in sectors such as banking, healthcare, supply chains, and retail, where secure sharing strengthens operational efficiency and trust. At the same time, adoption requires balancing privacy, accuracy, performance, and costs, which makes strategic choices essential. A thoughtful approach begins with mapping sensitive data, selecting the appropriate PETs, and aligning them with governance and compliance frameworks. This is where technological innovation meets organizational responsibility, creating the foundation for trusted collaboration. #PrivacyEnhancingTechnologies #DataSharing #DigitalTrust #Cybersecurity

Working with LLMs or AI chat tools? You’re probably leaking user data! Here’s the privacy hole no one’s talking about. When users interact with AI apps, they often share sensitive information like names, emails, internal identifiers, and even health records. Most apps send this raw data directly to the model. That means PII ends up in logs, audit trails, or third-party APIs. It’s a silent risk sitting in every prompt. Masking data sounds like a fix, but it often breaks the prompt or causes hallucinations. The model can’t reason properly if key context is missing. That’s where GPT Guard comes in. GPTGuard acts as a privacy layer that enables secure use of LLMs without ever exposing sensitive data to public models. Here's how it works: 1. PII Detection and Masking Every prompt is scanned for sensitive information using a mix of regex, heuristics, and AI models. Masking is handled through Protecto’s tokenization API, which replaces sensitive fields with format-preserving placeholders. This ensures nothing identifiable reaches the LLM. 2. Understanding Masked Inputs GPT Guard uses a fine-tuned OpenAI model that understands masked data. It preserves structure and type, so even a placeholder like `<PER>Token123</PER>` retains enough meaning for the LLM to respond naturally. The result: no hallucinations, no broken logic, just accurate answers with privacy intact. 3. Seamless Unmasking Once the LLM generates a reply, GPTGuard unmasks the tokens and returns a complete, readable response. The user never sees the masking — just the final answer with all original context restored. Key features: 🔍 Detects and masks sensitive data like PII, PHI, and internal identifiers from prompts and files 🚫 Prevents raw sensitive data from ever reaching the LLM 🔁 Unmasks the output so users still get a clear, readable response 🚀 Works with OpenAI, Claude, Gemini, Llama, DeepSeek, and other major LLMs 📄 Supports file uploads and secure chat with internal documents via RAG The best part? It works across cloud or on-prem, integrates cleanly with your existing workflows, and doesn't require custom fine-tuning or data pipelines.

Zero Trust Architecture for LLMs — Securing the Next Frontier of AI AI systems are powerful, but also risky. Large Language Models (LLMs) can expose sensitive data, misinterpret context, or be manipulated through prompt injection. That’s why Zero Trust for AI isn’t optional anymore — it’s essential. Here’s how a modern LLM stack can adopt a Zero Trust Architecture (ZTA) to stay secure from input to output. 1. Data Ingestion — Trust Nothing by Default 🔹Every input — whether human, application, or IoT sensor — must go through identity verification before login. 🔹 A policy engine evaluates user, device, and risk signals in real-time. No data flows unchecked. No implicit trust. 2. Identity and Access Management 🔹Implement Attribute-Based Access Control (ABAC) — access is granted based on who, what, and where. 🔹 Add Multi-Factor Authentication (MFA) and Just-in-Time provisioning to limit standing privileges. 🔹Combine these with a Zero Trust framework that authenticates every interaction — even inside your own network. 3. LLM Security Layer — Real-Time Defense LLMs are intelligent but vulnerable. They need a layered defense model that protects both inputs and outputs. This includes: 🔹Prompt filtering to prevent injection or manipulation 🔹Input validation to block malformed or unsafe data 🔹Data masking to remove sensitive information before processing 🔹Ethical guardrails to prevent biased or non-compliant responses 🔹Response filtering to ensure no sensitive or toxic output leaves the system This turns your LLM from a black box into a controlled, auditable system. 4. Core Zero Trust Principles for LLMs 🔹Verify explicitly — never assume identity or intent 🔹Assume breach — design as if every layer could be compromised 🔹Enforce least privilege — restrict what data, models, and prompts each actor can access When these principles are embedded into the model workflow, you achieve continuous verification — not one-time security. 5. Monitoring and Governance 🔹Security is not a one-time activity. 🔹Continuous policy configuration, monitoring, and threat detection keep your models aligned with compliance frameworks. 🔹Security policies evolve through a knowledge base that learns from incidents and new data. The result is a self-improving defense loop. => Why it Matters 🔹LLMs represent a new kind of attack surface — one that blends data, model logic, and user intent. 🔹Zero Trust ensures you control who interacts with your model, what they send, and what leaves the system. 🔹This mindset shifts AI from secure-perimeter thinking to secure-everywhere thinking. 🔹Every request is verified, every action is authorized, and every output is validated. How is your organization embedding Zero Trust principles into GenAI systems? Follow Rajeshwar D. for insights on AI/ML. #AI #LLM #ZeroTrust #CyberSecurity #GenAI #AIArchitecture #DataSecurity #PromptSecurity #AICompliance #AIGovernance

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

Sensitive data isn't always what many think it is.   Most people presume it’s limited to financial or health data.   Or credit card and social security numbers.   Then privacy laws came along and changed all of that.   Redefining sensitive data with varying definitions across different regulations.   And depending on the law, sensitive data may now include religious beliefs, over-the-counter med purchases, or precise geolocation data.   Different definitions, different requirements under different privacy laws....   And these discrepancies can lead to serious compliance risks and costly liabilities for businesses if data is not handled correctly within each jurisdiction.   It sure is confusing.   Yet, your company can manage sensitive data with these 4 steps:   1. Understand Your Data → Start by conducting a data inventory → Update the data inventory when new vendors, data processing activities, or technologies are introduced → Regularly assess whether current data collection aligns with business needs and legal requirements   2. Implement Privacy by Design Principles → Build privacy into your products or business systems proactively → Make privacy the default setting → Ensure security, transparency, and respect for user privacy   3. Be Proactive About Privacy Impact Assessments (PIAs) → Conduct a PIA to flag risks before new processes or technologies roll out → Meet legal requirements while enhancing efficiency, compliance, documentation, and transparency with governmental and public bodies → PIAs also help businesses address potential issues with cross-border data transfers   4. Take a Close Look at Your Data Retention Policies → Retain data only as long as needed → Document clear policies for how sensitive data will be deleted or anonymized when no longer needed → Address how privacy rights will be managed   Keep in mind: → Sensitive data needs to have a business purpose to be processed. → Sensitive data collection (and its purposes) need to be disclosed in privacy notices. → And some regulations have specific disclosure requirements around this.   🎉 Bonus tip: Align a likely security focused sensitive data policy with your privacy definitions of sensitive data! This is a common miss among companies and then what is sensitive data internally is confusing! Read our blog for more insights on sensitive data and how you can manage it. Link in the comments 👇  

Often, LLM innovation moves faster than our understanding of the security and privacy implications. Recently agent memory has come under scrutiny. ➡️ Researchers at MSU and UGA introduced MEXTRA, a method designed to extract private user data directly from LLM agent memory. On the surface, it sounds alarming 😱: - They extracted 1 in 4 private messages from a healthcare-focused AI agent. - Extraction succeeded in 83-87% of test cases, fully retrieving sensitive query histories. However, the paper misses a key point: It assumes a single memory store is shared across multiple users. Production-ready systems rarely use shared memory without user or session isolation. Services such as Zep AI (YC W24) have extensive support for this isolation, entirely circumventing the risk presented in the paper. If you’re already using such a service for agent memory, you’re ahead. But defense-in-depth is essential, particularly when dealing with sensitive health or financial data. Here's what developers can do in practice: - Sanitize and de-identify data before it hits memory. - Apply granular access control and strictly limit which agents (and humans) can query sensitive data and when. - Implement enhanced monitoring to catch unusual query patterns quickly, such as repeated and extensive query of the memory store. - Enforce query-level controls, limiting data returned per query to minimize risk. Research like this is useful—but context matters. When headlines raise alarms, read the original paper 🙂.

#30DaysOfGRC 16 Data classification is one of the most overlooked foundations in privacy and security. If your team doesn’t know what kind of data they are handling, how can they protect it? It’s not about a “confidential” label on every file. It’s about understanding the difference between what truly requires protection and what doesn’t. That customer support email? Probably low sensitivity. But a spreadsheet with birthdates and account details? That deserves stricter handling, limited access, and monitoring. Without a classification structure, teams rely on instinct. And instinct isn’t a substitute for policy. The way data flows through your systems should always align with its level of sensitivity. That one habit can prevent accidental leaks, misconfigurations, and unnecessary exposure. Teach your teams how to spot sensitive data. Don’t just give them a policy. Walk through examples. Show them what counts as restricted and what doesn’t. Governance works best when people actually understand what they are protecting. #30DaysOfGRC #DataPrivacy #CyberSecurity #GRC #InfoSec #RiskManagement #SecurityAwareness #TechPolicy #AIEthics

Be careful where you put your data. It’s tempting to drop confidential data into open AI tools for quick analysis or content generation. But here’s why that’s dangerous: • Open LLMs store prompts and responses. Your data could be retained, reviewed, or used for model training. • Data privacy and compliance risk. HIPAA, PCI, and internal confidentiality policies can be violated instantly. • Competitive exposure. You wouldn’t hand internal strategy decks to a stranger – treat open LLMs the same. How to do this securely: • Use enterprise AI platforms with private, encrypted deployments • Ensure no data retention policies are in place • Leverage local LLM models or cloud-based models within your secure environment • Consult your CISO or data privacy team before using generative AI with proprietary information We deploy AI for clients only in controlled, secure environments to protect their IP and customer data while delivering the efficiency gains AI offers. Don’t trade security for speed. If you want to implement AI safely within your organization, let’s connect. OTG Consulting #AI #DataPrivacy #Security #LLM #AIImplementation #Cybersecurity