Best Practices for Protecting 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.

As a lawyer who often dives deep into the world of data privacy, I want to delve into three critical aspects of data protection: A) Data Privacy This fundamental right has become increasingly crucial in our data-driven world. Key features include: -Consent and transparency: Organizations must clearly communicate how they collect, use, and share personal data. This often involves detailed privacy policies and consent mechanisms. -Data minimization: Companies should only collect data that's necessary for their stated purposes. This principle not only reduces risk but also simplifies compliance efforts. -Rights of data subjects: Under regulations like GDPR, individuals have rights such as access, rectification, erasure, and data portability. Organizations need robust processes to handle these requests. -Cross-border data transfers: With the invalidation of Privacy Shield and complexities around Standard Contractual Clauses, ensuring compliant data flows across borders requires careful legal navigation. B) Data Processing Agreements (DPAs) These contracts govern the relationship between data controllers and processors, ensuring regulatory compliance. They should include: -Scope of processing: DPAs must clearly define the types of data being processed and the specific purposes for which processing is allowed. -Subprocessor management: Controllers typically require the right to approve or object to any subprocessors, with processors obligated to flow down DPA requirements. -Data breach protocols: DPAs should specify timeframes for breach notification (often 24-72 hours) and outline the required content of such notifications, -Audit rights: Most DPAs now include provisions for audits and/or acceptance of third-party certifications like SOC II Type II or ISO 27001. C) Data Security These measures include: -Technical measures: This could involve encryption (both at rest and in transit), multi-factor authentication, and regular penetration testing. -Organizational measures: Beyond technical controls, this includes data protection impact assessments (DPIAs), appointing data protection officers where required, and maintaining records of processing activities. -Incident response plans: These should detail roles and responsibilities, communication protocols, and steps for containment, eradication, and recovery. -Regular assessments: This often involves annual security reviews, ongoing vulnerability scans, and updating security measures in response to evolving threats. These aren't just compliance checkboxes – they're the foundation of trust in the digital economy. They're the guardians of our digital identities, enabling the data-driven services we rely on while safeguarding our fundamental rights. Remember, in an era where data is often called the "new oil," knowledge of these concepts is critical for any organization handling personal data. #legaltech #innovation #law #business #learning

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.

Personal data is highly sensitive information we entrust to internet companies, and strong regulations require these companies to handle it safely and reliably to meet security, privacy, and compliance standards. In this tech blog, Airbnb’s data science team shares how they built a data classification workflow to establish a unified strategy for identifying and classifying data across all data stores. The workflow is built on three pillars: Catalog, Detection, and Reconciliation. The Catalog pillar focuses on creating a dynamic and accurate system to identify where data resides and organize it into a comprehensive inventory. Detection addresses the question: what data might be considered personal? This step involves a detection engine structured as a pipeline to scan, validate, and control thresholds for surfacing detected results. Finally, Reconciliation ensures accurate classification by involving data owners in a human-in-the-loop process to confirm or refine detected classifications. Given the complexity of the system, the team developed metrics to assess its quality. These metrics—recall, precision, and speed—evaluate how effectively, accurately, and efficiently the classification system operates, ensuring it safeguards personal data over the long term. Additionally, the team shares strategies for governing data classification early in the process, along with best practices for improving workflows. These insights provide a clear understanding of not only the metrics but also actionable ways to enhance classification systems. Highly recommended reading for anyone interested in data governance and security. #datascience #personal #data #governance #classification #metrics – – –  Check out the "Snacks Weekly on Data Science" podcast and subscribe, where I explain in more detail the concepts discussed in this and future posts:    -- Spotify: https://lnkd.in/gKgaMvbh   -- Apple Podcast: https://lnkd.in/gj6aPBBY    -- Youtube: https://lnkd.in/gcwPeBmR https://lnkd.in/gqxuQ29E

Many SMBs suffer from data hoarding tendencies - indiscriminately collecting and retaining any data they can get their hands on.  But this mindset is proving increasingly hazardous and expensive from a cybersecurity standpoint.  Over-retention of data exponentially increases your risk surface for breaches and compliance violations. The reality is that sometimes less is more when it comes to data.  Data minimization - limiting collection to what's required - is an underrated security best practice every organization should embrace. Think about it: The more data you hoard, the more avenues you open up for threat actors to steal sensitive info.  Plus, excess data complicates regulatory compliance regarding data handling. Data minimization starts with a thorough data-mapping exercise. Define clearly what data is genuinely required for your business processes versus what's superfluous. Establish strong access controls over essential data. But it doesn't stop there.  You must institutionalize continuous data pruning - systematically deleting outdated or unnecessary records. Implement data lifecycle policies with provisions for secure disposal. Kick that pack rat mentality.  Embrace a leaner data posture through minimization to reduce breach risks and costs.   Protecting a business is about knowing when to hold data and when to let it go. 

California's recent "do not sell" and "do not share" privacy enforcement sweep targeted streaming services, but it has relevant reminders and lessons for all companies.    1️⃣ "Selling" isn't just trading personal data for money--it can also be sharing data with vendors to make products work or for advertising. "Sharing" encompasses many data exchanges for #DigitalAdvertising.   2️⃣ "Selling" and "sharing" requires specific disclosures before the data is collected, including that the data will be sold or shared and opt-out process details.      3️⃣ Opt-out processes need to be available in the context that consumers interact with the company. Different processes may be required in-app, with connected services or devices, on websites, and in physical locations.   4️⃣ Opt-out processes need to be frictionless, with minimal steps to take.   5️⃣ Opt-out processes need stop the "sales" and "sharing" on a go forward basis across all methods by which the specific customer's #PersonalData is "sold" or "shared".    6️⃣ Starting late next month, detailed regulations regarding technical and operational processed to respond to, honor, and persist preferences (including for known customers) from opt-out signals like the #GlobalPrivacyControl become enforceable. To date, these regulations have been delayed by court order.   If your company has not looked at these issues recently, this quarter is a good time for a tune-up, especially with the California and Connecticut AG record of enforcement in this area, and the forthcoming Washington My Health My Data and #litigation risks that involves.   Here's a tune-up action plan:   ☑️Validate you understand all methods used to transmit data to third parties. Consider offline sharing, server-to-server integrations, SDKs in your apps, and #pixel/tracker/cookie based sharing. ☑️Confirm your process for identifying the third parties that data is disclosed to is current and working. ☑️Check in that protocols for disclosing data to third parties are defined and working, including with your opt-out processes. ☑️For necessary data disclosures that cannot be opted out of, test that #contracting processes are getting the necessary contract terms for sharing with those vendors and partners not to be a "sale" or "sharing" under the law. ☑️Confirm your data practices align with your commitments to customers (including in privacy policies, #cookiebanners, etc.). ☑️Probe that the methods in which customers provide data to your company that may be "sold" or "shared" are also contexts where they can opt-out. ☑️Explore the opt-out processes offered to determine that there isn't unnecessary friction. ☑️Test that your opt-out processes are working, including within the specified timelines.  ☑️Validate opt-out processes respond to the Global Privacy Control, adjusting as needed under privacy regulations such as to associate signals with known customer records. #MHMDA #privacy #privacyoperations #CCPA #donotsell

Yesterday, the FTC added to the ever-expanding jurisprudence of sensitive data.  By Final Order in X-Mode/Outlogic, the FTC has given a template for what the implementation of reasonable and appropriate safeguards might look like to avoid putting consumers’ sensitive personal information unlawfully at risk. Beyond not selling, sharing or transferring sensitive location data, X-Mode is required to: 1 - create a program to develop and maintain a comprehensive list of sensitive locations; 2 - delete or destroy all location data previously collected without consent (or deidentify/render non-sensitive); 3 – develop a supplier assessment program to assure lawful collection of data 4 – develop procedures to ensure data recipients lawfully use data sold or shared; 5 – implement a comprehensive privacy program, and 6 – create a data retention schedule. Without question, the FTC will expect businesses that collect, have, sell, share, transfer or otherwise obtain sensitive data and/or location data to follow most if not all of these or similar practices. https://lnkd.in/ecZ5aspU #safeguardprivacy #privacy #legaltech #compliance #adexchanger #iapp #dataprotection #onlineadvertising #ftc #tech #data #legal #advertising #law #compliance #ccpa #dataprotection #privacylaw #mobileadvertising #dataprivacy #advertisingandmarketing #technology #business #privacy #dataprotection #datatransfers Richy Glassberg, Katy Keohane, CIPP-US, Michael Simon, CIPP-US/E, CIPM,

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.

Data privacy might seem like a box to tick, but it’s much more than that. It’s the backbone of trust between you and your users. Here are a few ways to stay on top of it: + Encrypt sensitive data from day one to prevent unauthorized access. + Regular audits of your data storage and access systems are crucial to catch vulnerabilities before they become issues. + Be transparent about how you collect, store, and use data. Clear privacy policies go a long way in building user confidence. + Stay compliant with regulations like GDPR and CCPA. It’s not optional - it’s mandatory. + Train your team on the importance of data security, ensuring everyone from developers to support staff understands their role in safeguarding information. It’s easy to overlook these tasks when you're focused on growth. But staying proactive with data privacy isn’t just about following laws - it’s about protecting your reputation and building long-term relationships with your users. Don’t let what seems monotonous now turn into a crisis later. Stay ahead. #DataPrivacy #AppSecurity #GDPR #Trust #DataProtection #StartupTips #TechLeaders #CyberSecurity #UserTrust #AppDevelopment

𝐌𝐚𝐱𝐢𝐦𝐢𝐳𝐞 𝐃𝐚𝐭𝐚 𝐏𝐫𝐨𝐭𝐞𝐜𝐭𝐢𝐨𝐧 𝐰𝐢𝐭𝐡 𝐭𝐡𝐞 𝟑-𝟐-𝟏-𝟏-𝟎 𝐌𝐞𝐭𝐡𝐨𝐝: 𝐀 𝐃𝐢𝐠𝐢𝐭𝐚𝐥 𝐓𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦𝐚𝐭𝐢𝐨𝐧 𝐈𝐦𝐩𝐞𝐫𝐚𝐭𝐢𝐯𝐞 In the ever-evolving landscape of data security, adopting robust strategies is non-negotiable. Enter the 𝟑-𝟐-𝟏-𝟏-𝟎 𝐌𝐞𝐭𝐡𝐨𝐝, a powerful framework designed to fortify your data protection arsenal: 🔹 𝟑 𝐂𝐨𝐩𝐢𝐞𝐬: Ensure redundancy by maintaining three copies of your data across different systems or platforms.  🔸 𝐏𝐫𝐢𝐦𝐚𝐫𝐲 𝐂𝐨𝐩𝐲: Your primary working dataset.  🔸 𝐎𝐧-𝐬𝐢𝐭𝐞 𝐁𝐚𝐜𝐤𝐮𝐩: A secondary copy stored on-site for quick access and recovery.  🔸 𝐎𝐟𝐟-𝐬𝐢𝐭𝐞 𝐁𝐚𝐜𝐤𝐮𝐩: A tertiary copy stored off-site to safeguard against site-specific disasters. 🔹 𝟐 𝐒𝐭𝐨𝐫𝐚𝐠𝐞 𝐓𝐲𝐩𝐞𝐬: Diversify your storage infrastructure with at least two types (e.g., cloud, on-premises) to mitigate risks associated with single-point failures.  🔸 𝐂𝐥𝐨𝐮𝐝 𝐒𝐭𝐨𝐫𝐚𝐠𝐞: Leverage the scalability and accessibility of cloud-based solutions.  🔸 𝐎𝐧-𝐩𝐫𝐞𝐦𝐢𝐬𝐞𝐬 𝐒𝐭𝐨𝐫𝐚𝐠𝐞: Maintain control over sensitive data with on-site storage solutions. 🔹 𝟏 𝐎𝐟𝐟-𝐬𝐢𝐭𝐞 𝐁𝐚𝐜𝐤𝐮𝐩: Safeguard against site-specific disasters or disruptions by storing one copy of your data off-site.  🔸 𝐒𝐞𝐜𝐮𝐫𝐞 𝐃𝐚𝐭𝐚 𝐂𝐞𝐧𝐭𝐞𝐫: Partner with a trusted third-party provider to securely store your off-site backup.  🔸 𝐑𝐞𝐠𝐮𝐥𝐚𝐫 𝐑𝐨𝐭𝐚𝐭𝐢𝐨𝐧: Implement a rotation schedule to ensure data is up-to-date and accessible when needed. 🔹 𝟏 𝐈𝐦𝐦𝐮𝐭𝐚𝐛𝐥𝐞 𝐒𝐭𝐨𝐫𝐚𝐠𝐞: Implement immutable storage solutions to prevent unauthorized alterations or deletions, enhancing data integrity and compliance.  🔸 𝐖𝐎𝐑𝐌 (𝐖𝐫𝐢𝐭𝐞 𝐎𝐧𝐜𝐞 𝐑𝐞𝐚𝐝 𝐌𝐚𝐧𝐲): Utilize WORM technology to enforce data immutability and compliance with regulatory requirements.  🔸 𝐕𝐞𝐫𝐬𝐢𝐨𝐧 𝐂𝐨𝐧𝐭𝐫𝐨𝐥: Maintain a comprehensive version history to track changes and ensure data authenticity. 🔹 𝟎 𝐄𝐫𝐫𝐨𝐫𝐬: Regularly validate your backups and audit your storage systems to minimize the likelihood of errors or data corruption.  🔸 𝐀𝐮𝐭𝐨𝐦𝐚𝐭𝐞𝐝 𝐂𝐡𝐞𝐜𝐤𝐬: Implement automated backup verification processes to detect and rectify errors proactively.  🔸 𝐑𝐨𝐮𝐭𝐢𝐧𝐞 𝐀𝐮𝐝𝐢𝐭𝐬: Conduct regular audits of your storage infrastructure to identify vulnerabilities and ensure compliance with best practices. By embracing the 3-2-1-1-0 Method, you empower your organization to withstand a multitude of threats, from hardware failures to cyberattacks, ensuring business continuity and peace of mind. #AI #DataProtection #Cybersecurity #DigitalTransformation #GenerativeAI  #GenAI #Innovation #ArtificialIntelligence #ML  #ThoughtLeadership  #NiteshRastogiInsights  --------------------------------------------------- • Please 𝐋𝐢𝐤𝐞, 𝐒𝐡𝐚𝐫𝐞, 𝐂𝐨𝐦𝐦𝐞𝐧𝐭, 𝐒𝐚𝐯𝐞 if you find this post insightful • 𝐅𝐨𝐥𝐥𝐨𝐰 me on LinkedIn https://lnkd.in/gcy76JgE  • Ring the 🔔 for notifications!