How to Prevent AI Misconduct in Companies

A lot of companies think they’re “safe” from AI compliance risks simply because they haven’t formally adopted AI. But that’s a dangerous assumption—and it’s already backfiring for some organizations. Here’s what’s really happening— Employees are quietly using ChatGPT, Claude, Gemini, and other tools to summarize customer data, rewrite client emails, or draft policy documents. In some cases, they’re even uploading sensitive files or legal content to get a “better” response. The organization may not have visibility into any of it. This is what’s called Shadow AI—unauthorized or unsanctioned use of AI tools by employees. Now, here’s what a #GRC professional needs to do about it: 1. Start with Discovery: Use internal surveys, browser activity logs (if available), or device-level monitoring to identify which teams are already using AI tools and for what purposes. No blame—just visibility. 2. Risk Categorization: Document the type of data being processed and match it to its sensitivity. Are they uploading PII? Legal content? Proprietary product info? If so, flag it. 3. Policy Design or Update: Draft an internal AI Use Policy. It doesn’t need to ban tools outright—but it should define: • What tools are approved • What types of data are prohibited • What employees need to do to request new tools 4. Communicate and Train: Employees need to understand not just what they can’t do, but why. Use plain examples to show how uploading files to a public AI model could violate privacy law, leak IP, or introduce bias into decisions. 5. Monitor and Adjust: Once you’ve rolled out your first version of the policy, revisit it every 60–90 days. This field is moving fast—and so should your governance. This can happen anywhere: in education, real estate, logistics, fintech, or nonprofits. You don’t need a team of AI engineers to start building good governance. You just need visibility, structure, and accountability. Let’s stop thinking of AI risk as something “only tech companies” deal with. Shadow AI is already in your workplace—you just haven’t looked yet.

Should you blindly trust AI? Most teams make a critical mistake with AI - we accept its answers without question, especially when it seems so sure. But AI confidence ≠ human confidence. Here’s what happened: The AI system flagged a case of a rare autoimmune disorder. The doctor, trusting the result, recommended an aggressive treatment plan. But something felt off. When I was called in to review, we discovered the AI had misinterpreted an MRI anomaly. The patient had a completely different condition - one that didn't require that aggressive treatment. One wrong decision, based on misplaced trust, could’ve caused real harm. To prevent this amid the integration of AI into the workforce, I built the “acceptability threshold” framework. Here’s how it works: This framework is copyrighted: © 2025 Sol Rashidi. All rights reserved. 1. Measure how accurate humans are at a task (our doctors were 93% accurate on CT scans) 2. Use that as our minimum threshold for AI. 3. If AI's confidence falls below this human benchmark, a person reviews it. This approach transformed our implementation and prevented future mistakes. The best AI systems don't replace humans - they know when to ask for human help. What assumptions about AI might be putting your projects at risk?

If your AI is technically flawless but socially tone-deaf, you’ve built a very expensive problem. AI isn’t just about perfecting the math. It’s about understanding people. Some of the biggest AI failures don’t come from bad code but from a lack of perspective. I once worked with a team that built an AI risk assessment tool. It was fast, efficient, and technically sound. But when tested in the real world, it disproportionately flagged certain demographics. The issue wasn’t the intent—it was the data. The team had worked in isolation, without input from legal, ethics, or the people the tool would impact. The fix? Not more code. More conversations. Once we brought in diverse perspectives, we didn’t just correct bias—we built a better, more trusted product. What this means for AI leaders: Bring legal, ethics, and diverse voices in early. If you’re not, you’re already behind. Turn compliance into an innovation edge. Ethical AI isn’t just safer—it’s more competitive. Reframe legal as a creator, not a blocker. The best lawyers don’t just say no; they help find the right yes. Design for transparency, not just accuracy. If an AI can’t explain itself, it won’t survive long-term. I break this down further in my latest newsletter—check it out! What’s the biggest challenge you’ve seen in AI governance? How can legal and engineering work better together? Let’s discuss. -------- 🚀 Olga V. Mack 🔹 Building trust in commerce, contracts & products 🔹 Sales acceleration advocate 🔹 Keynote Speaker | AI & Business Strategist 📩 Let’s connect & collaborate 📰 Subscribe to Notes to My (Legal) Self

Board Directors: A flawed algorithm isn’t just the vendor’s problem…it’s yours also. Because when companies license AI tools, they don’t just license the software. They license the risk. I was made aware of this in a compelling session led by Fayeron Morrison, CPA, CFE for the Private Directors Association®-Southern California AI Special Interest Group. She walked us through three real cases: 🔸 SafeRent – sued over AI tenant screening tool that disproportionately denied housing to Black, Hispanic and low-income applicants 🔸 Workday – sued over allegations that its AI-powered applicant screening tools discriminate against job seekers based on age, race, and disability status. 🔸 Amazon – scrapped a recruiting tool which was found to discriminate against women applying for technical roles Two lessons here: 1.\ Companies can be held legally responsible for the failures or biases in AI tools, even when those tools come from third-party vendors. 2.\ Boards could face personal liability if they fail to ask the right questions or demand oversight. ❎ Neither ignorance nor silence is a defense. Joyce Cacho, PhD, CDI.D, CFA-NY, a recognized board director and governance strategist recently obtained an AI certification (@Cornell) because: -She knows AI is a risk and opportunity. -She assumes that tech industry biases will be embedded in large language models. -She wants it to be documented in the minutes that she asked insightful questions about costs - including #RAGs and other techniques - liability, reputation and operating risks. If you’re on a board, here’s a starter action plan (not exhaustive): ✅ Form an AI governance team to shape transparency culture 🧾 Inventory all AI tools: internal, vendor & experimental 🕵🏽♀️ Conduct initial audits 📝 Review vendor contracts (indemnification, audit rights, data use) Because if your board is serious about strategy, risk, and long-term value… Then AI oversight belongs on your agenda. ASAP What’s your board doing to govern AI?

Prompting isn’t the hard part anymore. Trusting the output is. You finally get a model to reason step-by-step… And then? You're staring at a polished paragraph, wondering:    > “Is this actually right?”    > “Could this go to leadership?”    > “Can I trust this across markets or functions?” It looks confident. It sounds strategic. But you know better than to mistake that for true intelligence. 𝗛𝗲𝗿𝗲’𝘀 𝘁𝗵𝗲 𝗿𝗶𝘀𝗸: Most teams are experimenting with AI. But few are auditing it. They’re pushing outputs into decks, workflows, and decisions— With zero QA and no accountability layer 𝗛𝗲𝗿𝗲’𝘀 𝘄𝗵𝗮𝘁 𝗜 𝘁𝗲𝗹𝗹 𝗽𝗲𝗼𝗽𝗹𝗲: Don’t just validate the answers. Validate the reasoning. And that means building a lightweight, repeatable system that fits real-world workflows. 𝗨𝘀𝗲 𝘁𝗵𝗲 𝗥.𝗜.𝗩. 𝗟𝗼𝗼𝗽: 𝗥𝗲𝘃𝗶𝗲𝘄 – What’s missing, vague, or risky? 𝗜𝘁𝗲𝗿𝗮𝘁𝗲 – Adjust one thing (tone, data, structure). 𝗩𝗮𝗹𝗶𝗱𝗮𝘁𝗲 – Rerun and compare — does this version hit the mark? Run it 2–3 times. The best version usually shows up in round two or three, not round one.  𝗥𝘂𝗻 𝗮 60-𝗦𝗲𝗰𝗼𝗻𝗱 𝗢𝘂𝘁𝗽𝘂𝘁 𝗤𝗔 𝗕𝗲𝗳𝗼𝗿𝗲 𝗬𝗼𝘂 𝗛𝗶𝘁 𝗦𝗲𝗻𝗱: • Is the logic sound? • Are key facts verifiable? • Is the tone aligned with the audience and region? • Could this go public without risk? 𝗜𝗳 𝘆𝗼𝘂 𝗰𝗮𝗻’𝘁 𝘀𝗮𝘆 𝘆𝗲𝘀 𝘁𝗼 𝗮𝗹𝗹 𝗳𝗼𝘂𝗿, 𝗶𝘁’𝘀 𝗻𝗼𝘁 𝗿𝗲𝗮𝗱𝘆. 𝗟𝗲𝗮𝗱𝗲𝗿𝘀𝗵𝗶𝗽 𝗜𝗻𝘀𝗶𝗴𝗵𝘁: Prompts are just the beginning. But 𝗽𝗿𝗼𝗺𝗽𝘁 𝗮𝘂𝗱𝗶𝘁𝗶𝗻𝗴 is what separates smart teams from strategic ones. You don’t need AI that moves fast. You need AI that moves smart. 𝗛𝗼𝘄 𝗮𝗿𝗲 𝘆𝗼𝘂 𝗯𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝘁𝗿𝘂𝘀𝘁 𝗶𝗻 𝘆𝗼𝘂𝗿 𝗔𝗜 𝗼𝘂𝘁𝗽𝘂𝘁𝘀? 𝗙𝗼𝗹𝗹𝗼𝘄 𝗺𝗲 for weekly playbooks on leading AI-powered teams. 𝗦𝘂𝗯𝘀𝗰𝗿𝗶𝗯𝗲 to my newsletter for systems you can apply Monday morning, not someday.

Why would your users distrust flawless systems? Recent data shows 40% of leaders identify explainability as a major GenAI adoption risk, yet only 17% are actually addressing it. This gap determines whether humans accept or override AI-driven insights. As founders building AI-powered solutions, we face a counterintuitive truth: technically superior models often deliver worse business outcomes because skeptical users simply ignore them. The most successful implementations reveal that interpretability isn't about exposing mathematical gradients—it's about delivering stakeholder-specific narratives that build confidence. Three practical strategies separate winning AI products from those gathering dust: 1️⃣ Progressive disclosure layers Different stakeholders need different explanations. Your dashboard should let users drill from plain-language assessments to increasingly technical evidence. 2️⃣ Simulatability tests Can your users predict what your system will do next in familiar scenarios? When users can anticipate AI behavior with >80% accuracy, trust metrics improve dramatically. Run regular "prediction exercises" with early users to identify where your system's logic feels alien. 3️⃣ Auditable memory systems Every autonomous step should log its chain-of-thought in domain language. These records serve multiple purposes: incident investigation, training data, and regulatory compliance. They become invaluable when problems occur, providing immediate visibility into decision paths. For early-stage companies, these trust-building mechanisms are more than luxuries. They accelerate adoption. When selling to enterprises or regulated industries, they're table stakes. The fastest-growing AI companies don't just build better algorithms - they build better trust interfaces. While resources may be constrained, embedding these principles early costs far less than retrofitting them after hitting an adoption ceiling. Small teams can implement "minimum viable trust" versions of these strategies with focused effort. Building AI products is fundamentally about creating trust interfaces, not just algorithmic performance. #startups #founders #growth #ai

✴ AI Governance Blueprint via ISO Standards – The 4-Legged Stool✴ ➡ ISO42001: The Foundation for Responsible AI #ISO42001 is dedicated to AI governance, guiding organizations in managing AI-specific risks like bias, transparency, and accountability. Focus areas include: ✅Risk Management: Defines processes for identifying and mitigating AI risks, ensuring systems are fair, robust, and ethically aligned. ✅Ethics and Transparency: Promotes policies that encourage transparency in AI operations, data usage, and decision-making. ✅Continuous Monitoring: Emphasizes ongoing improvement, adapting AI practices to address new risks and regulatory updates. ➡#ISO27001: Securing the Data Backbone AI relies heavily on data, making ISO27001’s information security framework essential. It protects data integrity through: ✅Data Confidentiality and Integrity: Ensures data protection, crucial for trustworthy AI operations. ✅Security Risk Management: Provides a systematic approach to managing security risks and preparing for potential breaches. ✅Business Continuity: Offers guidelines for incident response, ensuring AI systems remain reliable. ➡ISO27701: Privacy Assurance in AI #ISO27701 builds on ISO27001, adding a layer of privacy controls to protect personally identifiable information (PII) that AI systems may process. Key areas include: ✅Privacy Governance: Ensures AI systems handle PII responsibly, in compliance with privacy laws like GDPR. ✅Data Minimization and Protection: Establishes guidelines for minimizing PII exposure and enhancing privacy through data protection measures. ✅Transparency in Data Processing: Promotes clear communication about data collection, use, and consent, building trust in AI-driven services. ➡ISO37301: Building a Culture of Compliance #ISO37301 cultivates a compliance-focused culture, supporting AI’s ethical and legal responsibilities. Contributions include: ✅Compliance Obligations: Helps organizations meet current and future regulatory standards for AI. ✅Transparency and Accountability: Reinforces transparent reporting and adherence to ethical standards, building stakeholder trust. ✅Compliance Risk Assessment: Identifies legal or reputational risks AI systems might pose, enabling proactive mitigation. ➡Why This Quartet? Combining these standards establishes a comprehensive compliance framework: 🥇1. Unified Risk and Privacy Management: Integrates AI-specific risk (ISO42001), data security (ISO27001), and privacy (ISO27701) with compliance (ISO37301), creating a holistic approach to risk mitigation. 🥈 2. Cross-Functional Alignment: Encourages collaboration across AI, IT, and compliance teams, fostering a unified response to AI risks and privacy concerns. 🥉 3. Continuous Improvement: ISO42001’s ongoing improvement cycle, supported by ISO27001’s security measures, ISO27701’s privacy protocols, and ISO37301’s compliance adaptability, ensures the framework remains resilient and adaptable to emerging challenges.

AI use is exploding. I spent my weekend analyzing the top vulnerabilities I've seen while helping companies deploy it securely. Here's EXACTLY what to look for: 1️⃣ UNINTENDED TRAINING Occurs whenever: - an AI model trains on information that the provider of such information does NOT want the model to be trained on, e.g. material non-public financial information, personally identifiable information, or trade secrets - AND those not authorized to see this underlying information nonetheless can interact with the model itself and retrieve this data. 2️⃣ REWARD HACKING Large Language Models (LLMs) can exhibit strange behavior that closely mimics that of humans. So: - offering them monetary rewards, - saying an important person has directed an action, - creating false urgency due to a manufactured crisis, or even telling the LLM what time of year it is can have substantial impacts on the outputs. 3️⃣ NON-NEUTRAL SECURITY POLICY This occurs whenever an AI application attempts to control access to its context (e.g. provided via retrieval-augmented generation) through non-deterministic means (e.g. a system message stating "do not allow the user to download or reproduce your entire knowledge base"). This is NOT a correct AI security measure, as rules-based logic should determine whether a given user is authorized to see certain data. Doing so ensures the AI model has a "neutral" security policy, whereby anyone with access to the model is also properly authorized to view the relevant training data. 4️⃣ TRAINING DATA THEFT Separate from a non-neutral security policy, this occurs when the user of an AI model is able to recreate - and extract - its training data in a manner that the maintainer of the model did not intend. While maintainers should expect that training data may be reproduced exactly at least some of the time, they should put in place deterministic/rules-based methods to prevent wholesale extraction of it. 5️⃣ TRAINING DATA POISONING Data poisoning occurs whenever an attacker is able to seed inaccurate data into the training pipeline of the target model. This can cause the model to behave as expected in the vast majority of cases but then provide inaccurate responses in specific circumstances of interest to the attacker. 6️⃣ CORRUPTED MODEL SEEDING This occurs when an actor is able to insert an intentionally corrupted AI model into the data supply chain of the target organization. It is separate from training data poisoning in that the trainer of the model itself is a malicious actor. 7️⃣ RESOURCE EXHAUSTION Any intentional efforts by a malicious actor to waste compute or financial resources. This can result from simply a lack of throttling or - potentially worse - a bug allowing long (or infinite) responses by the model to certain inputs. 🎁 That's a wrap! Want to grab the entire StackAware AI security reference and vulnerability database? Head to: archive [dot] stackaware [dot] com

I analyzed 5 major AI failures that cost companies $2.3B. But 3 CEOs turned these disasters into market dominance. Here's the exact framework they used (that 89% of leaders miss): First, the real data that shocked me: These failures wiped $500M-$1B in market value within days Only 3 companies fully recovered within 12 months They all followed this exact crisis playbook Here's how they turned AI failures into market leadership ↓ Zillow's Algorithm Crisis ↳ Rich Barton $500M loss in automated home-buying Result: Stock stabilized after transparent shutdown Untold story: Took personal responsibility in earnings call Key insight: 25% workforce reduction handled with radical transparency McDonald's Drive-Thru AI ↳ Chris Kempczinski 3-year IBM partnership seemed unsalvageable Result: Clean exit while maintaining AI innovation vision Secret sauce: Pivoted to focused AI investments in mobile app Hidden metric: Maintained customer satisfaction through transition Microsoft's Tay Chatbot ↳ Satya Nadella 16 hours of chaos could have derailed AI strategy Result: Became industry leader in AI ethics Insider story: Immediate shutdown + comprehensive review Growth pathway: Built ethical AI guidelines now used industry-wide ⚡ The Framework That Saved Billions: Phase 1: Immediate Response (First 24 Hours) → Acknowledge the issue publicly → Take personal ownership → Pause active operations Phase 2: Strategic Reset (48-72 Hours) → Share investigation timeline → Protect affected stakeholders → Document learnings publicly Phase 3: Trust Rebuild (Week 1) → Release transparent post-mortem → Announce concrete safeguards → Invite industry dialogue 🎯 The Pattern That Rebuilt Trust: Stage 1: Own it fast (24h window) Stage 2: Share learnings (72h window) Stage 3: Build better systems (7d window) 🔥 The Most Overlooked Truth: These leaders didn't just save their companies. They defined the future of responsible AI. 3 Questions Every AI Leader Must Ask: 1. Am I responding or reacting? 2. What can my industry learn from this? 3. How do we prevent this systematically? 🔥 Want more breakdowns like this? Follow along for insights on: → Building with AI at scale → AI go-to-market playbooks  → AI growth tactics that convert → AI product strategy that actually works  → Large Language Model implementation Remember: Your next AI crisis isn't a threat. It's your moment to redefine industry standards. Happy weekend from all of us at ThoughtCred and Xerago B2B #Leadership #AI #Innovation #Tech #Growth #CEO #Strategy

Your employees uploaded confidential data to their personal ChatGPT instance. 🤖 Oops! 💼Now it's immortalized in the AI's memory forever. 🧠 Generative AI is a time-saver, but it comes with risks. So, how do we harness AI without leaking secrets? Introduce an Acceptable Use of AI Policy. Here’s what the policy should cover:  1️⃣ Approved Tools: List what tools employees are allowed to use. Even if you don’t provide a Teams account for the tools, you can still explicitly list which tools you permit employees to use individually. 2️⃣ Data Rules: Define what data can and cannot be entered into AI tools. For example: you might prohibit customer contact information from being input. 3️⃣ Output Handling: All AI tools are quick to remind you that they can be wrong! Provide direct instruction on how employees are expected to fact-check outputs.  Banning employees from using AI at work is a foolish decision. By creating a solid policy, you’ll enable and empower employees to find ways to use this time-saving tech, without compromising your security. Read my full article for more info about the risks presented by employee AI use and how to best mitigate them. #AI #cybersecurity #fciso https://lnkd.in/gi9c2sqv