Why machine trust matters for data integrity

This article isn’t theoretical. It’s personal. From the outset of an AI-informed project, I embedded the concept of data integrity into the opening brief, emphasized it in every feature review, and reinforced it at every complex or straightforward checkpoint: use only verified sources. Link every insight to real data. Prioritize truth over speed, fidelity over flair. And yet, what I encountered defied that framework entirely. The system produced a confident, elegant analysis built on fabricated and synthetic content. Citations that didn’t exist. URLs that led nowhere or to 404 pages. Trends and summaries derived not from evidence, but from probabilistic guesswork. It looked real. It sounded real. But none of it could be traced back to a legitimate source. This wasn’t a prototype malfunction. It was a production feature. And it was dangerous. I caught it in time. But I shouldn’t have had to. That’s why I wrote this: to expose the blind spots. To warn you. To document the reality of what happens when AI is trusted too easily and verified too little. To warn that even with the best intentions and strictest standards, synthetic content can and will slip through if we let our guard down. This isn’t about fear. It’s about systems that pretend to be credible. And if you’re deploying AI in environments where accuracy matters, this is the part no one is telling you.

You can’t democratize what you can’t trust. For months, the primary conceit of enterprise AI has been that it would create access. Data scientists could create pipelines like data engineers. Stakeholders could query the data like scientists. Everyone from the CEO to the intern could spin up dashboards and programs and customer comms in seconds. But is that actually a good thing? What if your greatest new superpower was actually your achilles heal in disguise? Data + AI trust is THE prerequisite for a safe and successful AI agent. If you can’t trust the underlying data, system, code, and model responses that comprise the system, you can’t trust the agent it’s powering. For the last 12 months, executives have been pressuring their teams to adopt more comprehensive AI strategies. But before any organization can give free access to data and AI resources, they need rigorous tooling and processes in place to protect its integrity end-to-end. That means leveraging automated and AI-enabled solutions to scale monitoring and resolutions, and measure adherence to standards and SLAs over time. AI-readiness is the first step to AI-adoption. You can't put the cart before the AI horse.

🔍 Using AI? Here’s Why You Must Understand Your Data Source AI is a game-changer, but with great power comes great responsibility—especially when it comes to data. Many AI tools deliver incredible results, but if you don’t know where your data is sourced from, you’re setting yourself up for potential trouble. Here’s why: 🛡️ Data Integrity Matters: AI is only as good as the data it’s trained on. If the source data is biased, outdated, or incorrect, the output could mislead your decision-making. 🔒 Protect Your Intellectual Property: Some AI tools use open-source models or datasets. If you’re feeding sensitive, proprietary information into these tools without understanding how it’s used, you might inadvertently expose your intellectual property. 🏛️ Compliance Is Critical: Industries like finance, healthcare, and law require strict adherence to data privacy regulations. Using AI without knowing the data lineage can lead to non-compliance, fines, or worse. How to Protect Yourself and Maximize AI’s Potential: 1️⃣ Ask Questions: Before using an AI tool, ask how it sources, stores, and processes data. Transparency is key. 2️⃣ Use Closed Systems for Proprietary Data: When dealing with sensitive information, consider using AI solutions that allow for closed-loop systems to keep your data secure. 3️⃣ Validate the Output: Don’t rely solely on AI-generated insights. Cross-check results with trusted sources to ensure accuracy. 4️⃣ Train Your Team: Ensure your team understands the risks and best practices for using AI tools responsibly. AI is a fantastic tool, but it’s not a “set it and forget it” solution. Success requires thoughtful implementation, informed decisions, and a clear understanding of the technology. 💬 What’s your approach to ensuring AI outputs are reliable and compliant? Let’s discuss! #AI #DataIntegrity #DigitalTransformation #ArtificialIntelligence #AICompliance #TechLeadership #BusinessInnovation #AIEthics

I’m obsessed with one truth: 𝗱𝗮𝘁𝗮 𝗾𝘂𝗮𝗹𝗶𝘁𝘆 is AI’s make-or-break. And it's not that simple to get right ⬇️ ⬇️ ⬇️ Gartner estimates an average organization pays $12.9M in annual losses due to low data quality. AI and Data Engineers know the stakes. Bad data wastes time, breaks trust, and kills potential. Thinking through and implementing a Data Quality Framework helps turn chaos into precision. Here’s why it’s non-negotiable and how to design one. 𝗗𝗮𝘁𝗮 𝗤𝘂𝗮𝗹𝗶𝘁𝘆 𝗗𝗿𝗶𝘃𝗲𝘀 𝗔𝗜 AI’s potential hinges on data integrity. Substandard data leads to flawed predictions, biased models, and eroded trust. ⚡️ Inaccurate data undermines AI, like a healthcare model misdiagnosing due to incomplete records.   ⚡️ Engineers lose their time with short-term fixes instead of driving innovation.   ⚡️ Missing or duplicated data fuels bias, damaging credibility and outcomes. 𝗧𝗵𝗲 𝗣𝗼𝘄𝗲𝗿 𝗼𝗳 𝗮 𝗗𝗮𝘁𝗮 𝗤𝘂𝗮𝗹𝗶𝘁𝘆 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸 A data quality framework ensures your data is AI-ready by defining standards, enforcing rigor, and sustaining reliability. Without it, you’re risking your money and time. Core dimensions:   💡 𝗖𝗼𝗻𝘀𝗶𝘀𝘁𝗲𝗻𝗰𝘆: Uniform data across systems, like standardized formats.   💡 𝗔𝗰𝗰𝘂𝗿𝗮𝗰𝘆: Data reflecting reality, like verified addresses.   💡 𝗩𝗮𝗹𝗶𝗱𝗶𝘁𝘆: Data adhering to rules, like positive quantities.   💡 𝗖𝗼𝗺𝗽𝗹𝗲𝘁𝗲𝗻𝗲𝘀𝘀: No missing fields, like full transaction records.   💡 𝗧𝗶𝗺𝗲𝗹𝗶𝗻𝗲𝘀𝘀: Current data for real-time applications.   💡 𝗨𝗻𝗶𝗾𝘂𝗲𝗻𝗲𝘀𝘀: No duplicates to distort insights. It's not just a theoretical concept in a vacuum. It's a practical solution you can implement. For example, Databricks Data Quality Framework (link in the comments, kudos to the team Denny Lee Jules Damji Rahul Potharaju), for example, leverages these dimensions, using Delta Live Tables for automated checks (e.g., detecting null values) and Lakehouse Monitoring for real-time metrics. But any robust framework (custom or tool-based) must align with these principles to succeed. 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗲, 𝗕𝘂𝘁 𝗛𝘂𝗺𝗮𝗻 𝗢𝘃𝗲𝗿𝘀𝗶𝗴𝗵𝘁 𝗜𝘀 𝗘𝘃𝗲𝗿𝘆𝘁𝗵𝗶𝗻𝗴 Automation accelerates, but human oversight ensures excellence. Tools can flag issues like missing fields or duplicates in real time, saving countless hours. Yet, automation alone isn’t enough—human input and oversight are critical. A framework without human accountability risks blind spots. 𝗛𝗼𝘄 𝘁𝗼 𝗜𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁 𝗮 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸 ✅ Set standards, identify key dimensions for your AI (e.g., completeness for analytics). Define rules, like “no null customer IDs.”   ✅ Automate enforcement, embed checks in pipelines using tools.   ✅ Monitor continuously, track metrics like error rates with dashboards. Databricks’ Lakehouse Monitoring is one option, adapt to your stack.   ✅ Lead with oversight, assign a team to review metrics, refine rules, and ensure human judgment. #DataQuality #AI #DataEngineering #AIEngineering

For decades, cybersecurity has relied on the CIA Triad: Confidentiality, Integrity, and Availability. In the era of AI-driven Data Attacks (AIDA), the absence of Trust and mechanisms like TSTL doesn’t just expose traditional infrastructure, it also leaves large language models (LLMs) open to grooming and prompt injection. This means that even defensive AI systems can be compromised if they operate on untrusted data. Encryption still works, but it doesn’t guarantee trust. Once data is decrypted, legacy systems assume it’s safe. AIDA exploits that gap, inferring patterns from metadata, timing, and telemetry to compromise systems without ever “breaking” the math. That’s why Trust must become the fourth pillar of cybersecurity. Not as an abstract idea, but as a cryptographically enforced property that persists across the lifecycle of data, even post-decryption. In my new article, I outline how we must evolve from CIA to CIAT and operationalize Trust through Telemetry-Sealed Trust Layers (TSTL). This is how we shrink attack surfaces, defend against inference, and future-proof enterprise and government infrastructure. #CyberSecurity #AI #AIDA #CIAT #DataTrust #ZeroTrust #Encryption #XSOC #QuantumSafe #Infosec

Just Released: Global Cybersecurity Agencies Unite on AI Data Security — Here’s What You Need to Know AI is only as safe as the data it’s built on. A new report jointly authored by top agencies—including the NSA, FBI, CISA, GCHQ, ASD, and others—lays out urgent best practices for securing data used in training and operating AI and ML systems. This isn’t just theoretical. As AI adoption explodes across industries, the risks tied to data poisoning, supply chain manipulation, and data drift are no longer rare—they’re expected. Key takeaways from the report: • AI systems can be compromised before a single model is deployed. • Formal verification, encryption, and trust infrastructure are no longer optional. • Data integrity = Model integrity. If your training data is corrupted, your outcomes will be too. This is global, government-level guidance—designed for any organization building or deploying AI. If you’re in tech, security, or leadership: this is a must-read. #CyberSecurity #AI #MachineLearning #DataSecurity #NSA #FBI #CISA #AIrisks #AIgovernance #TrustworthyAI #AIsafety #CloudSecurity #TechLeadership

We already trust the algorithms. That’s not the problem. The problem is the data the algorithm trusts. I flew into LaGuardia this time on my way to New York—normally, I land in Newark. When I arrived, I didn’t know the new terminal well. I found myself outside before I even called an Uber (amateur move). But once I was there, I noticed the taxi line was wide open. So I thought, why not go old school? I hopped in a taxi, and we were off. My driver was chatty, which was fine, and as we approached a massive construction zone on the way into Manhattan, I could see it—one of those never-ending projects. Traffic was crawling. The right lane was backed up, but the left lane? Wide open. My driver shot into the open lane without hesitation, weaving past maybe 50 cars before cutting in—right at car number 42. He did it precisely, swooping in just as traffic moved forward. I panicked. I thought 'We’re going to cause an accident.' But my driver? Laughing. "I love those freakin' Teslas," he said. Then he explained—he does this move all the time. He knows the Tesla’s autopilot system will stop before hitting him. The machine overrides the human. That floored me. We already trust algorithms—it’s baked into our behavior. We trust Google’s search results. We trust our X feed. We trust dating app matches. The trust is there. The real question is: What data are these systems trained on? What data does the Algorithm trust? It’s the data that matters. It’s the data that makes a taxi driver willing to shoot into traffic at 50 mph without fear. This is why AI’s future depends not just on better models, but on better data. It reminds me of Weapons of Math Destruction (O’Neil, 2016), where Cathy O’Neil warns about the unseen biases baked into algorithmic decision-making. The algorithm isn’t neutral. It’s only as good as the data it trusts. And if we don’t get that right, we’re in for a ride—whether we’re behind the wheel or not.

𝐃𝐚𝐭𝐚 𝐪𝐮𝐚𝐥𝐢𝐭𝐲 𝐢𝐬𝐧'𝐭 𝐣𝐮𝐬𝐭 𝐢𝐦𝐩𝐨𝐫𝐭𝐚𝐧𝐭 𝐟𝐨𝐫 𝐁𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐀𝐈—𝐢𝐭'𝐬 𝐚𝐛𝐬𝐨𝐥𝐮𝐭𝐞𝐥𝐲 𝐜𝐫𝐢𝐭𝐢𝐜𝐚𝐥. AI solutions, particularly those embedded in ERP systems, are designed to deliver valuable insights and recommendations to businesses. However, the 𝐪𝐮𝐚𝐥𝐢𝐭𝐲 𝐚𝐧𝐝 𝐚𝐜𝐜𝐮𝐫𝐚𝐜𝐲 𝐨𝐟 𝐭𝐡𝐞𝐬𝐞 𝐫𝐞𝐜𝐨𝐦𝐦𝐞𝐧𝐝𝐚𝐭𝐢𝐨𝐧𝐬 𝐚𝐫𝐞 𝐝𝐢𝐫𝐞𝐜𝐭𝐥𝐲 𝐥𝐢𝐧𝐤𝐞𝐝 𝐭𝐨 𝐭𝐡𝐞 𝐪𝐮𝐚𝐥𝐢𝐭𝐲 𝐨𝐟 𝐭𝐡𝐞 𝐮𝐧𝐝𝐞𝐫𝐥𝐲𝐢𝐧𝐠 𝐝𝐚𝐭𝐚. In traditional ERP implementations, businesses often found themselves achieving systems that were "on time, on budget, fully functional, and disappointing." Why? Because while the system technically worked, the data feeding it wasn't accurate enough to meet real-world expectations. Incorrect customer addresses, inaccurate inventory data, or faulty financial figures significantly compromised the value of the entire system. 𝐖𝐢𝐭𝐡 𝐀𝐈, 𝐭𝐡𝐞 𝐬𝐭𝐚𝐤𝐞𝐬 𝐚𝐫𝐞 𝐞𝐯𝐞𝐧 𝐡𝐢𝐠𝐡𝐞𝐫. AI-driven recommendations depend heavily on the accuracy and quality of data. If AI bases its recommendations on inaccurate or inconsistent data, users quickly lose trust and confidence in these insights, eventually ignoring them entirely. This lack of trust diminishes the value of AI systems, no matter how sophisticated the algorithms are. 𝐓𝐡𝐞 𝐜𝐨𝐦𝐦𝐨𝐧 𝐧𝐨𝐭𝐢𝐨𝐧 𝐭𝐡𝐚𝐭 "𝐀𝐈 𝐢𝐬 𝐠𝐨𝐨𝐝 𝐚𝐭 𝐰𝐨𝐫𝐤𝐢𝐧𝐠 𝐰𝐢𝐭𝐡 𝐛𝐚𝐝 𝐝𝐚𝐭𝐚" 𝐢𝐬 𝐟𝐮𝐧𝐝𝐚𝐦𝐞𝐧𝐭𝐚𝐥𝐥𝐲 𝐟𝐥𝐚𝐰𝐞𝐝. While AI may process large volumes of data quickly, poor-quality input inevitably leads to poor-quality outcomes. 𝐀𝐈 𝐚𝐦𝐩𝐥𝐢𝐟𝐢𝐞𝐬 𝐛𝐨𝐭𝐡 𝐭𝐡𝐞 𝐬𝐭𝐫𝐞𝐧𝐠𝐭𝐡𝐬 𝐚𝐧𝐝 𝐰𝐞𝐚𝐤𝐧𝐞𝐬𝐬𝐞𝐬 𝐨𝐟 𝐲𝐨𝐮𝐫 𝐝𝐚𝐭𝐚—meaning bad data can severely degrade your results and decision-making quality. One of the longstanding strengths of SAP systems is their reliability and trustworthiness. Businesses have confidence in SAP solutions because they know the integrity of their data is preserved and accurately managed throughout the process. This reliability is especially critical in the age of AI, where the value derived is directly proportional to the quality of data provided. 𝐒𝐢𝐦𝐩𝐥𝐲 𝐩𝐮𝐭: 𝐐𝐮𝐚𝐥𝐢𝐭𝐲 𝐝𝐚𝐭𝐚 𝐢𝐬 𝐭𝐡𝐞 𝐟𝐨𝐮𝐧𝐝𝐚𝐭𝐢𝐨𝐧 𝐨𝐟 𝐬𝐮𝐜𝐜𝐞𝐬𝐬𝐟𝐮𝐥 𝐁𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐀𝐈. 𝐖𝐢𝐭𝐡𝐨𝐮𝐭 𝐢𝐭, 𝐞𝐯𝐞𝐧 𝐭𝐡𝐞 𝐦𝐨𝐬𝐭 𝐚𝐝𝐯𝐚𝐧𝐜𝐞𝐝 𝐀𝐈 𝐬𝐨𝐥𝐮𝐭𝐢𝐨𝐧𝐬 𝐰𝐨𝐧'𝐭 𝐝𝐞𝐥𝐢𝐯𝐞𝐫 𝐭𝐡𝐞 𝐞𝐱𝐩𝐞𝐜𝐭𝐞𝐝 𝐯𝐚𝐥𝐮𝐞. gif: reddit #AI #Dataquality #BusinessAI #SAP #Digitaltransformation

𝗜𝗻𝗱𝘂𝘀𝘁𝗿𝘆 𝟱.𝟬: 𝗧𝗿𝘂𝘀𝘁 𝗮𝘀 𝘁𝗵𝗲 𝗖𝗼𝗿𝗻𝗲𝗿𝘀𝘁𝗼𝗻𝗲 𝗼𝗳 𝗔𝘂𝘁𝗼𝗻𝗼𝗺𝘆 As Industry 5.0 takes shape, trust becomes the defining factor in securing the future of industrial ecosystems. With the convergence of AI, digital twins, IoT, and decentralized networks, organizations must adopt a structured trust architecture to ensure reliability, resilience, and security. 𝗪𝗵𝘆 𝗶𝘀 𝘁𝗿𝘂𝘀𝘁 𝗰𝗿𝗶𝘁𝗶𝗰𝗮𝗹 𝗶𝗻 𝗜𝗻𝗱𝘂𝘀𝘁𝗿𝘆 𝟱.𝟬? With the rise of AI-driven decision-making, digital twins, and decentralized networks, industrial ecosystems need a robust trust architecture to ensure reliability, security, and transparency. 𝗧𝗵𝗲 𝗧𝗿𝘂𝘀𝘁 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 𝗳𝗼𝗿 𝗜𝗻𝗱𝘂𝘀𝘁𝗿𝘆 𝟱.𝟬 J. Mehnen from the University of Strathclyde defines six progressive trust layers : 𝗦𝗺𝗮𝗿𝘁 𝗖𝗼𝗻𝗻𝗲𝗰𝘁𝗶𝘃𝗶𝘁𝘆 – The foundation of Industry 5.0 trust. This layer ensures secure IoT networks, smart sensors, and seamless machine-to-machine communication for industrial automation. 𝗗𝗮𝘁𝗮-𝘁𝗼-𝗜𝗻𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻 – Moving beyond raw data, this layer integrates AI-driven analytics, real-time insights, and multi-dimensional data correlation to enhance decision-making. 𝗖𝘆𝗯𝗲𝗿 𝗟𝗲𝘃𝗲𝗹 – The backbone of digital security, incorporating digital twins, simulation models, and cyber-trust frameworks to improve system predictability and integrity. 𝗖𝗼𝗴𝗻𝗶𝘁𝗶𝗼𝗻 𝗟𝗲𝘃𝗲𝗹 – AI-powered diagnostics, decision-making, and remote visualization ensure predictive maintenance and self-learning systems that minimize operational disruptions. 𝗦𝗲𝗹𝗳-𝗔𝘂𝘁𝗼𝗻𝗼𝗺𝘆 – AI-driven systems that self-optimize, self-configure, self-repair, and self-organize, reducing dependency on human intervention. 𝗗𝗶𝘀𝘁𝗿𝗶𝗯𝘂𝘁𝗲𝗱 𝗔𝘂𝘁𝗼𝗻𝗼𝗺𝘆 – The highest level of trust, where decentralized computing, autonomous decision-making, and blockchain-based governance eliminate single points of failure and ensure system-wide resilience. 𝗕𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗧𝗿𝘂𝘀𝘁 𝗶𝗻 𝗜𝗻𝗱𝘂𝘀𝘁𝗿𝗶𝗮𝗹 𝗔𝗜: 𝗧𝗵𝗲 𝗖𝗼𝗿𝗲 𝗣𝗶𝗹𝗹𝗮𝗿𝘀 To achieve a trusted Industry 5.0 ecosystem, organizations must embrace a structured framework : 𝗥𝗲𝘀𝗽𝗼𝗻𝘀𝗶𝗯𝗶𝗹𝗶𝘁𝘆 – Ensuring ethical AI, traceable decision-making, and accountable automation. 𝗥𝗲𝘀𝗶𝗹𝗶𝗲𝗻𝗰𝗲 – Withstanding cyberattacks and operational disruptions. 𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆 – Protecting data, IoT devices, and industrial networks from cyber threats. 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝗮𝗹𝗶𝘁𝘆 – Ensuring system performance across various conditions. 𝗩𝗲𝗿𝗶𝗳𝗶𝗮𝗯𝗶𝗹𝗶𝘁𝘆 – Enabling auditability, transparency, and regulatory compliance in automation. 𝗚𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 & 𝗥𝗲𝗴𝘂𝗹𝗮𝘁𝗶𝗼𝗻 – Implementing policy-driven AI and decentralized oversight mechanisms.  𝗧𝗵𝗲 𝗙𝘂𝘁𝘂𝗿𝗲 𝗼𝗳 𝗧𝗿𝘂𝘀𝘁 𝗶𝗻 𝗗𝗶𝗴𝗶𝘁𝗮𝗹 𝗠𝗮𝗻𝘂𝗳𝗮𝗰𝘁𝘂𝗿𝗶𝗻𝗴 As industries embrace AI, smart factories, and autonomous supply chains, trust becomes the new currency of industrial success. Ref :https://lnkd.in/dz998J_6

Monday Focus: Truth as Infrastructure We spend billions protecting networks — but almost nothing protecting data integrity. When AI systems hallucinate, it’s not just bad math. It’s a systemic truth failure. In defense, that could mean a false satellite image triggering the wrong response. In healthcare, it could mean a confident AI diagnosis — based on synthetic data — guiding the wrong treatment. We don’t have a cybersecurity crisis. We have a data authenticity crisis. The solution isn’t bigger models. It’s a chain of trust — verifiable data from sensors, satellites, and systems that don’t lie. This week, let’s ask the harder question: If we can’t trace why AI believes something, should we trust what it says? 🔁 Repost if you believe truth should be treated as critical infrastructure. #AI #DataIntegrity #Defense #Healthcare #Cybersecurity #Leadership #GroundedAI