How Data Alteration Impacts Professional Trust

As information security practitioners, we are entrusted with the critical responsibility of protecting the confidentiality, integrity, and availability (CIA) of data. While each component of the CIA triad is essential, today I want to focus on the importance of INTEGRITY and why it must never be overlooked.  Confidentiality ensures that sensitive information is accessed only by authorised personnel. Availability guarantees that the information and systems are accessible to those who need them when they need them. INTEGRITY ensures that data remains accurate, consistent, and trustworthy throughout its lifecycle. It is the foundation upon which critical decisions are made, from patient care to financial transactions. When integrity is compromised, the consequences can be devastating. Take the tragic case of the Therac-25 medical radiation incidents. 💉 Between 1985 and 1987, six patients suffered severe radiation overdoses due to a combination of software bugs and design flaws in the Therac-25 machine. These incidents highlight the dire consequences of failing to maintain the integrity of systems and data. Read more about the incident here: https://lnkd.in/gey8kk4c To uphold integrity, consider these actionable steps: 🔶 Tighten access controls and authentication mechanisms 🔶Rigorously test and validate systems before any update goes live—lessons learned from Therac-25 🔶 Establish Secure System Configurations (system hardening, regular patches, monitor systems, etc.) 🔶 Deploy Detective Controls (system audits, file integrity checkers, and antivirus systems to identify and alert on unauthorised changes) 🔶 Establish clear incident response and recovery procedures  🔶And importantly, cultivate a culture of integrity. Set the standard high and lead by example, emphasising integrity in every decision In the private sector, compromised integrity can lead to financial losses, reputational damage, and legal liabilities. Imagine the chaos that would ensue if a bank's transaction records were altered or corrupted. In the public sector, the stakes are even higher. Inaccurate or tampered data could lead to miscarriages of justice, compromised national security, or erosion of public trust.  #IntegrityMatters #CIATriad #InfoSecEssentials

The Illusion of Control Pretending in Business Forecasts Mirrors the Deceptive World of Filters and AI In today's digital age, technology allows us to enhance our images, applying filters or even AI-generated effects to make everything look perfect. This tendency to create illusions isn’t just limited to social media – it’s creeping into the business world too. Some professionals are tweaking their forecasts, business plans, and reports to present a more favorable picture than reality. But while editing a photo may seem harmless, altering business data can lead to serious consequences. The Risks of Faking It: Relying solely on secondary data or asking generic questions through AI tools without deeper investigation or verification is like using a photo without proper copyright—both lack thoroughness and can lead to significant issues down the line. When we edit business forecasts to appear better than they are, we create a false sense of security. This illusion can mislead decision-makers, leading to poor choices that harm the company in the long run. Just as a heavily filtered image hides flaws, a falsified report masks real risks. Dangers of falsifying or Exaggerating business data: * Loss of Trust: If stakeholders discover the data has been manipulated, trust is broken. This loss of confidence can drive away investors, customers, and even employees. * Missed Opportunities: Inaccurate forecasts can prevent businesses from seizing opportunities or addressing challenges in time. The false picture creates complacency. * Increased Risk: When a company relies on fake data, it neglects proper risk management. This makes it vulnerable to financial losses and operational problems. * Legal and Ethical Issues: Falsifying business information can lead to legal trouble and damage a company's ethical reputation. 👉 The Consequences of Deception Just as filters distort our appearance, fake business reports distort reality. Pretending that everything is fine doesn’t change the facts – it only delays the inevitable. The truth will come out, and when it does, the fallout will be far worse than if the issues had been addressed honestly from the start. The effects of this deception are wide-ranging. Trust erodes, decisions are based on faulty information, and the business may collapse under the weight of its own lies. 👉 A Call for Authenticity To build a successful business, we must prioritize transparency. Accurate data-driven decisions lead to sustainable growth, while honesty earns long-term trust. Just as we should accept ourselves without filters, we should embrace the truth in business without manipulation. In the end, the truth – in both photos and business reports – is the strongest foundation. Let’s choose authenticity over illusion and build businesses that can truly last. #forecast #planning #businessplanning #businessforecast #transparency #authenticity #integrity #trust #decisionmaking #leadership #management #finance #marketing #strategy

SMO owner debarment order by FDA. FDA Investigator Craig Garmendia highlights the following: “The SMO owner’s debarment is tied to egregious violations of data integrity standards, which are the cornerstone of ethical and effective clinical research. Accurate and reliable data in clinical trials is essential to: • Ensure Patient Safety: Clinical trial results directly influence patient care and the approval of life-saving treatments. Manipulated or falsified data can lead to harmful outcomes for patients. • Uphold Regulatory Compliance: Data integrity violations undermine trust in the regulatory system, delaying approval of safe and effective therapies. • Protect Public Trust: The credibility of clinical research depends on adherence to ethical practices. Violations erode trust in the healthcare system and its ability to prioritize patient well-being.”

The 70% vs 16% Lesson: Why Data Transparency Matters Early in my career, I walked into an executive meeting, ready to present, and confident about our performance. As we went through the presentation, I approached our slide with the metrics. “Our win rate is 70%,” I announced proudly. Then, one of the executives stopped me and said, “Actually, we have your win rate as 16%, but I'm sure there are just some minor differences in our calculations.” The room went quiet, and I continued with my presentation. Same data. Same company. Completely different numbers. The difference? The filters, assumptions, and methodology behind each calculation. My 70% included qualifiers, exclusions, filters, and business logic that painted a rosier picture. Their 16% represented the raw, unfiltered reality. The lesson that stuck with me was that when presenting data, always include: 1. The specific filters you applied 2. Any exclusions or limitations 3. The methodology behind your calculations 4. The time period and scope of your analysis Data without context isn’t insight—it’s just numbers that can mislead. Different teams analyzing the same dataset often reach different conclusions based on their assumptions and filters. Your integrity as a data professional depends on transparency. Present the full picture, not just the favorable one. Your credibility and your team’s trust depend on it. What’s the most important data lesson you’ve learned in your career? #EGDataGuy #dataintegrity #businessanalytics

It just became more difficult to trust LLMs in healthcare. Since a recent study showed a shocking fact: it’s easier to corrupt LLMs than previously expected.  Just a small number of corrupted samples can sabotage LLMs. And therefore manipulate the output of the LLM.  Ultimately risking impacting patient care, when used in healthcare. We’ve often gotten the impression that more data is better.  But bigger models trained on more data are equally vulnerable to small amounts of poisoned dat LLM models can be exploited during the AI model training and fine-tuning.  Even tiny amounts of poisoned data can implant hidden backdoors that trigger harmful AI behaviors.  250 poison samples can compromise the models, independent on model and dataset sizes. Data poisoning attacks or deliberate corruptions of AI training data, can: - Sabotage your AI models - Cause misdiagnoses or wrong treatments - Harmful recommendations - Risk patient safety  - Disrupt workflows - Misallocate resources - Cause misinformation So, the new study from Anthropic should not be ignored. Especially when operating in healthcare. Here are some of my reflections: 1. Data poisoning risk being ignored by many healthcare AI projects 2. AI model size does not guarantee immunity from poisoning 3. Patient safety consequences can be severe but subtle 4. Security investments often miss data integrity aspects 5. Regulatory frameworks lag behind new AI vulnerabilities AI systems influencing millions of patients depend on accurate training data. We cannot accept this risk when using LLMs, or other AI tools in patient care. We need to: 1. Implemented strict data validation pipelines 2. Developed continuous AI model monitoring systems 3. Improve staff training on AI threat awareness 4. Collaborated cross-sector on AI governance 5. Invested in research on AI attack mitigation It will be increasingly important to ensure data quality for LLMs.  Especially in fields where patient outcomes could be affected. If patients and healthcare professionals don’t trust the data or the outcome, the tool will die. Understanding data poisoning will be critical for healthcare leaders who want to implement AI safely. How are you preparing for a safe implementation?