Best Practices for Data Trust Signals

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.

Can you truly trust your data if you don’t have robust data quality controls, systematic audits, and regular cleanup practices in place? 🤔 The answer is a resounding no! Without these critical processes, even the most sophisticated systems can misguide you, making your insights unreliable and potentially harmful to decision-making. Data quality controls are your first line of defense, ensuring that the information entering your system meets predefined standards and criteria. These controls prevent the corruption of your database from the first step, filtering out inaccuracies and inconsistencies. 🛡️ Systematic audits take this a step further by periodically scrutinizing your data for anomalies that might have slipped through initial checks. This is crucial because errors can sometimes be introduced through system updates or integration points with other data systems. Regular audits help you catch these issues before they become entrenched problems. Cleanup practices are the routine maintenance tasks that keep your data environment tidy and functional. They involve removing outdated, redundant, or incorrect information that can skew analytics and lead to poor business decisions. 🧹 Finally, implementing audit dashboards can provide a real-time snapshot of data health across platforms, offering visibility into ongoing data quality and highlighting areas needing attention. This proactive approach not only maintains the integrity of your data but also builds trust among users who rely on this information to make critical business decisions. Without these measures, trusting your data is like driving a car without ever servicing it—you’re heading for a breakdown. So, if you want to ensure your data is a reliable asset, invest in these essential data hygiene practices. 🚀 #DataQuality #RevOps #DataGovernance

Can You Trust Your Data the Way You Trust Your Best Team Member? Do you know the feeling when you walk into a meeting and rely on that colleague who always has the correct information? You trust them to steer the conversation, to answer tough questions, and to keep everyone on track. What if data could be the same way—reliable, trustworthy, always there when you need it? In business, we often talk about data being "the new oil," but let’s be honest: without proper management, it’s more like a messy garage full of random bits and pieces. It’s easy to forget how essential data trust is until something goes wrong—decisions are based on faulty numbers, reports are incomplete, and suddenly, you’re stuck cleaning up a mess. So, how do we ensure data is as trustworthy as that colleague you rely on? It starts with building a solid foundation through these nine pillars: ➤ Master Data Management (MDM): Consider MDM the colleague who always keeps the big picture in check, ensuring everything aligns and everyone is on the same page.     ➤ Reference Data Management (RDM): Have you ever been in a meeting where everyone uses a different term for the same thing? RDM removes the confusion by standardising key data categories across your business. ➤ Metadata Management: Metadata is like the notes and context we make on a project. It tracks how, when, and why decisions were made, so you can always refer to them later.     ➤ Data Catalog: Imagine a digital filing cabinet that’s not only organised but searchable, easy to navigate, and quick to find exactly what you need.     ➤ Data Lineage: This is your project’s timeline, tracking each step of the data’s journey so you always know where it has been and is going.     ➤ Data Versioning: Data evolves as we update project plans. Versioning keeps track of every change so you can revisit previous versions or understand shifts when needed.     ➤ Data Provenance: Provenance is the backstory—understanding where your data originated helps you assess its trustworthiness and quality.     ➤ Data Lifecycle Management: Data doesn’t last forever, just like projects have deadlines. Lifecycle management ensures your data is used and protected appropriately throughout its life.     ➤ Data Profiling: Consider profiling a health check for your data, spotting potential errors or inconsistencies before they affect business decisions. When we get these pillars right, data goes from being just a tool to being a trusted ally—one you can count on to help make decisions, drive strategies, and ultimately support growth. So, what pillar would you focus on to make your data more trustworthy? Cheers! Deepak Bhardwaj

Want a simple way to earn trust from your stakeholders, analysts? Send them data quality alerts when things go wrong. This is data 101 for engineers, but my team and I are citizen developers. We don't have the same kind of training - things like this simply aren't immediately obvious to us. Here's an example of why you should do this, from just this week: An analysis that we run depends on A LOT of inputs, including some manually uploaded files. Lots of opportunity for things to go wrong. On Monday, I heard from one of the file providers that her upload had been failing for almost 2 weeks. One of my end users spotted the problem at about the same time that I heard from my file provider. Not great being the last one to find out about a data quality problem in an analysis that you're responsible for. I had been working on some data quality alerts, and sure enough, they would have spotted the problem right away. So I'm eager to finalize them and get them into production. Here are some easy things I'm implementing: 1. Record count checks: do today's inputs have roughly the same number of records as yesterday's? This doesn't catch all problems, but it's very easy to implement - it's all I needed to spot the problem I just described. 2. Consistency check: Make sure your inputs "look" the way you expect them to. In this case, the reason the file upload was failing was that one of the columns in the file changed from being numerical to text, and our SQL database didn't like that. 3. Check for null values: You might get the right number of records and the right data types, but the data could all be null. 4. Automated alerts: You don't want to hear from your stakeholders about data quality issues the way that I did. Put in some basic alerts like these with automatic emails when they're triggered. Copy all your stakeholders. This will sound remedial to data engineers, but these are habits that we citizen developers don't always have. There's a lot that we citizen developers can learn from our friends in IT, and simple things like this can go away toward earning our stakeholders' trust. #citizendevelopment #lowcode #nocode #analytics #supplychainanalytics

At its core, data quality is an issue of trust. As organizations scale their data operations, maintaining trust between stakeholders becomes critical to effective data governance. Three key stakeholders must align in any effective data governance framework: 1️⃣ Data consumers (analysts preparing dashboards, executives reviewing insights, and marketing teams relying on events to run campaigns) 2️⃣ Data producers (engineers instrumenting events in apps) 3️⃣ Data infrastructure teams (ones managing pipelines to move data from producers to consumers) Tools like RudderStack’s managed pipelines and data catalogs can help, but they can only go so far. Achieving true data quality depends on how these teams collaborate to build trust. Here's what we've learned working with sophisticated data teams: 🥇 Start with engineering best practices: Your data governance should mirror your engineering rigor. Version control (e.g. Git) for tracking plans, peer reviews for changes, and automated testing aren't just engineering concepts—they're foundations of reliable data. 🦾 Leverage automation: Manual processes are error-prone. Tools like RudderTyper help engineering teams maintain consistency by generating analytics library wrappers based on their tracking plans. This automation ensures events align with specifications while reducing the cognitive load of data governance. 🔗 Bridge the technical divide: Data governance can't succeed if technical and business teams operate in silos. Provide user-friendly interfaces for non-technical stakeholders to review and approve changes (e.g., they shouldn’t have to rely on Git pull requests). This isn't just about ease of use—it's about enabling true cross-functional data ownership. 👀 Track requests transparently: Changes requested by consumers (e.g., new events or properties) should be logged in a project management tool and referenced in commits. ‼️ Set circuit breakers and alerts: Infrastructure teams should implement circuit breakers for critical events to catch and resolve issues promptly. Use robust monitoring systems and alerting mechanisms to detect data anomalies in real time. ✅ Assign clear ownership: Clearly define who is responsible for events and pipelines, making it easy to address questions or issues. 📄Maintain documentation: Keep standardized, up-to-date documentation accessible to all stakeholders to ensure alignment. By bridging gaps and refining processes, we can enhance trust in data and unlock better outcomes for everyone involved. Organizations that get this right don't just improve their data quality–they transform data into a strategic asset. What are some best practices in data management that you’ve found most effective in building trust across your organization? #DataGovernance #Leadership #DataQuality #DataEngineering #RudderStack

After 8+ years of experience working closely with dozens of data partners to integrate 40+ data sources into one platform, we've learned: 1. Invest in relationships Focus on trust before technology. Connect, feed people, show up, operate with integrity, apologize, fix it when you get it wrong, do excellent work...It's not complicated, but it takes time. 2. Educate on what's legally and technically possible Most concerns about data sharing are born from a place of confusion or lack of knowledge. When you aren't sure what's legal or ethical, then you are usually more risk averse. 3. Write strong and clear data sharing agreements Make agreements clear, solid, and simple. People feel better when they understand what the boundaries of the partnership look like. Strong agreements aren't an indication of a lack of faith. In fact, the opposite is true. The clearer your agreements, the more trust you can build with partners. 4. Show why it matters Don't just extract value. Deliver value back. Create win-wins. That always makes sharing more fun. What's your take? How can the anti-trafficking movement build strong, trust-based data sharing partnerships? #data #lighthouse #humantrafficking

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.

Some data headaches are really just trust issues in disguise. Let me explain: I once met with a medical device company’s President who complained endlessly about their on-prem ETL failures and nightly data fires. On the surface, it was a purely technical problem - broken scripts, crashing servers, and no backup plan. But as I asked more questions, I realized the true pain was deeper... Nobody trusted the numbers. Reports conflicted, definitions varied, and decisions were stalled or based on gut feel. In short, they had no data management strategy. Every stakeholder boiled their frustrations down to “broken servers,” when the real issue was the foundation. Helping the president of the company see this issue helped us close the deal. So, here’s the takeaway... Before you dive into code fixes, pause and ask: Do people actually trust these numbers? If they don’t, no amount of faster queries will solve the real problem. Build trust by: 1. Defining consistent metrics and ownership 2. Establishing lightweight data governance (even a small team can make a big difference) 3. Validating data end-to-end to ensure accuracy Fix the foundation first, and the rest will follow. ♻️ Share if you know a data leader who needs to address the trust gap. Follow me for more on building data strategies that drive real business impact.

A hairdresser and a marketer came into the bar. Hold on… Haircuts and marketing? 🤔 Here's the reality: Consumers are more aware than ever of how their data is used. User privacy is no longer a checkbox – It is a trust-building cornerstone for any online business. 88% of consumers say they won’t share personal information unless they trust a brand. Think about it: Every time a user visits your website, they’re making an active choice to trust you or not. They want to feel heard and respected. If you're not prioritizing their privacy preferences, you're risking their data AND loyalty. We’ve all been there – Asked for a quick trim and got VERY short hair instead. Using consumers’ data without consent is just like cutting the hair you shouldn’t cut. That horrible bad haircut ruined our mood for weeks. And a poor data privacy experience can drive customers straight to your competitors, leaving your shopping carts empty. How do you avoid this pitfall? - Listen to your users. Use consent and preference management tools such as Usercentrics to allow customers full control of their data. - Be transparent. Clearly communicate how you use their information and respect their choices. - Build trust: When users feel secure about their data, they’re more likely to engage with your brand. Make sure your website isn’t alienating users with poor data practices. Start by evaluating your current approach to data privacy by scanning your website for trackers. Remember, respecting consumer choices isn’t just an ethical practice. It’s essential for long-term success in e-commerce. Focus on creating a digital environment where consumers feel valued and secure. Trust me, it will pay off! 💰

Data in AI: The Core of Risk, the Heart of Trust In “AI Risk Management – Thinking Beyond Regulatory Boundaries”, the Cloud Security Alliance shifts the conversation from just regulations to real-world resilience — and data sits at the center of it all. Key insights from the guide: ** Data ≠ Just Inputs ** — It’s the foundation for AI’s behavior, decisions, and risks. If the data is biased, outdated, or poorly sourced, the system can’t be trusted — no matter how g ood the algorithm is. ** Organic vs. Synthetic **— Auditors must understand why certain data types were chosen, assess their origin, and validate whether synthetic patterns introduce unintended behaviors. ** Lineage & Transparency — From black-market datasets to IP risks, data pedigree is no longer optional. Traceability is a must. ** Privacy Tension — The paradox of "right to be forgotten" vs "mandatory to remember" (per GDPR vs EU AI Act) creates architectural and compliance challenges that demand strategic thinking. ** Copyright, Consent & Control — Training data must not only be accurate, but ethically and legally sourced. The audit must confirm this. If you're building or auditing AI, and not actively assessing data provenance, quality, and governance — you're flying blind. 🔗 Read the guide: https://lnkd.in/gyu3gRpz 💬 How is your organization evolving its data practices to build truly trustworthy AI? #AI #RiskManagement #TrustworthyAI #AIaudit #DataGovernance #SyntheticData #DataLineage #CloudSecurityAlliance #AIGovernance #AIPrivacy #ResponsibleAI #AIML