Data Privacy Issues With AI

AI is not failing because of bad ideas; it’s "failing" at enterprise scale because of two big gaps: 👉 Workforce Preparation 👉 Data Security for AI While I speak globally on both topics in depth, today I want to educate us on what it takes to secure data for AI—because 70–82% of AI projects pause or get cancelled at POC/MVP stage (source: #Gartner, #MIT). Why? One of the biggest reasons is a lack of readiness at the data layer. So let’s make it simple - there are 7 phases to securing data for AI—and each phase has direct business risk if ignored. 🔹 Phase 1: Data Sourcing Security - Validating the origin, ownership, and licensing rights of all ingested data. Why It Matters: You can’t build scalable AI with data you don’t own or can’t trace. 🔹 Phase 2: Data Infrastructure Security - Ensuring data warehouses, lakes, and pipelines that support your AI models are hardened and access-controlled. Why It Matters: Unsecured data environments are easy targets for bad actors making you exposed to data breaches, IP theft, and model poisoning. 🔹 Phase 3: Data In-Transit Security - Protecting data as it moves across internal or external systems, especially between cloud, APIs, and vendors. Why It Matters: Intercepted training data = compromised models. Think of it as shipping cash across town in an armored truck—or on a bicycle—your choice. 🔹 Phase 4: API Security for Foundational Models - Safeguarding the APIs you use to connect with LLMs and third-party GenAI platforms (OpenAI, Anthropic, etc.). Why It Matters: Unmonitored API calls can leak sensitive data into public models or expose internal IP. This isn’t just tech debt. It’s reputational and regulatory risk. 🔹 Phase 5: Foundational Model Protection - Defending your proprietary models and fine-tunes from external inference, theft, or malicious querying. Why It Matters: Prompt injection attacks are real. And your enterprise-trained model? It’s a business asset. You lock your office at night—do the same with your models. 🔹 Phase 6: Incident Response for AI Data Breaches - Having predefined protocols for breaches, hallucinations, or AI-generated harm—who’s notified, who investigates, how damage is mitigated. Why It Matters: AI-related incidents are happening. Legal needs response plans. Cyber needs escalation tiers. 🔹 Phase 7: CI/CD for Models (with Security Hooks) - Continuous integration and delivery pipelines for models, embedded with testing, governance, and version-control protocols. Why It Matter: Shipping models like software means risk comes faster—and so must detection. Governance must be baked into every deployment sprint. Want your AI strategy to succeed past MVP? Focus and lock down the data. #AI #DataSecurity #AILeadership #Cybersecurity #FutureOfWork #ResponsibleAI #SolRashidi #Data #Leadership

By next year we will be producing as much data every 15 minutes as all of human civilisation did up to the year 2003. Data might be the new oil, but it’s unrefined. AI companies are the new oil refineries. Many companies are quietly changing their Terms and Privacy Policies to allow them use this data for machine learning, and the FTC weighed in on this in a blog post last week. This suggests that organisations reviewing their policies and documentation when it comes to AI and data protection in particular, and more broadly - T&Cs and contracts, need to be mindful about how AI is addressed. In their recent blog on the subject, the FTC says: “It may be unfair or deceptive for a company to adopt more permissive data practices—for example, to start sharing consumers’ data with third parties or using that data for AI training—and to only inform consumers of this change through a surreptitious, retroactive amendment to its terms of service or privacy policy.” The temptation for companies to unilaterally amend their privacy policies for broader data utilisation is palpable, driven by the dual forces of business incentive and technological evolution. However, such surreptitious alterations, aimed at circumventing user backlash, tread dangerously close to legal and ethical boundaries. We have already seen major companies fall foul of consumer backlash when they attempted to change their terms along these lines. Historically, the FTC in the US has taken a firm stance against what they deem deceptive practices. Cases like Gateway Learning Corporation and a notable genetic testing company underscore the legal repercussions that await businesses reneging on their privacy commitments. These precedents serve as a stark reminder of the legal imperatives that bind companies to their original user agreements. The EU context is also worth considering. The GDPR's implications for AI and technology companies are significant, particularly in its requirements for transparent data processing, the necessity of informed consent, and the rights of data subjects to object to data processing. For companies, this means navigating a labyrinth of legal obligations that mandate not only the protection of user data but also ensure that any changes to privacy policies are communicated clearly. The intersection of GDPR with the FTC's stance on privacy policy amendments seems to highlight a consensus on the importance of data protection and the rights of consumers in the digital marketplace. This synergy between the U.S. and EU approach creates a formidable legal landscape that AI companies must navigate with caution and respect for user privacy. The path forward for AI companies is clear: transparency is a key element in AI Governance upon which AI and data policies are built. It is arguably the most important element in the AI Act, and it is emerging as a key component in global legislation as jurisdications develop their own AI regulations.

This new white paper by Stanford Institute for Human-Centered Artificial Intelligence (HAI) titled "Rethinking Privacy in the AI Era" addresses the intersection of data privacy and AI development, highlighting the challenges and proposing solutions for mitigating privacy risks. It outlines the current data protection landscape, including the Fair Information Practice Principles, GDPR, and U.S. state privacy laws, and discusses the distinction and regulatory implications between predictive and generative AI. The paper argues that AI's reliance on extensive data collection presents unique privacy risks at both individual and societal levels, noting that existing laws are inadequate for the emerging challenges posed by AI systems, because they don't fully tackle the shortcomings of the Fair Information Practice Principles (FIPs) framework or concentrate adequately on the comprehensive data governance measures necessary for regulating data used in AI development. According to the paper, FIPs are outdated and not well-suited for modern data and AI complexities, because: - They do not address the power imbalance between data collectors and individuals. - FIPs fail to enforce data minimization and purpose limitation effectively. - The framework places too much responsibility on individuals for privacy management. - Allows for data collection by default, putting the onus on individuals to opt out. - Focuses on procedural rather than substantive protections. - Struggles with the concepts of consent and legitimate interest, complicating privacy management. It emphasizes the need for new regulatory approaches that go beyond current privacy legislation to effectively manage the risks associated with AI-driven data acquisition and processing. The paper suggests three key strategies to mitigate the privacy harms of AI: 1.) Denormalize Data Collection by Default: Shift from opt-out to opt-in data collection models to facilitate true data minimization. This approach emphasizes "privacy by default" and the need for technical standards and infrastructure that enable meaningful consent mechanisms. 2.) Focus on the AI Data Supply Chain: Enhance privacy and data protection by ensuring dataset transparency and accountability throughout the entire lifecycle of data. This includes a call for regulatory frameworks that address data privacy comprehensively across the data supply chain. 3.) Flip the Script on Personal Data Management: Encourage the development of new governance mechanisms and technical infrastructures, such as data intermediaries and data permissioning systems, to automate and support the exercise of individual data rights and preferences. This strategy aims to empower individuals by facilitating easier management and control of their personal data in the context of AI. by Dr. Jennifer King Caroline Meinhardt Link: https://lnkd.in/dniktn3V

This Stanford study examined how six major AI companies (Anthropic, OpenAI, Google, Meta, Microsoft, and Amazon) handle user data from chatbot conversations.  Here are the main privacy concerns. 👀 All six companies use chat data for training by default, though some allow opt-out 👀 Data retention is often indefinite, with personal information stored long-term 👀 Cross-platform data merging occurs at multi-product companies (Google, Meta, Microsoft, Amazon) 👀 Children's data is handled inconsistently, with most companies not adequately protecting minors 👀 Limited transparency in privacy policies, which are complex and hard to understand and often lack crucial details about actual practices Practical Takeaways for Acceptable Use Policy and Training for nonprofits in using generative AI: ✅ Assume anything you share will be used for training - sensitive information, uploaded files, health details, biometric data, etc. ✅ Opt out when possible - proactively disable data collection for training (Meta is the one where you cannot) ✅ Information cascades through ecosystems - your inputs can lead to inferences that affect ads, recommendations, and potentially insurance or other third parties ✅ Special concern for children's data - age verification and consent protections are inconsistent Some questions to consider in acceptable use policies and to incorporate in any training. ❓ What types of sensitive information might your nonprofit staff  share with generative AI?  ❓ Does your nonprofit currently specifically identify what is considered “sensitive information” (beyond PID) and should not be shared with GenerativeAI ? Is this incorporated into training? ❓ Are you working with children, people with health conditions, or others whose data could be particularly harmful if leaked or misused? ❓ What would be the consequences if sensitive information or strategic organizational data ended up being used to train AI models? How might this affect trust, compliance, or your mission? How is this communicated in training and policy? Across the board, the Stanford research points that developers’ privacy policies lack essential information about their practices. They recommend policymakers and developers address data privacy challenges posed by LLM-powered chatbots through comprehensive federal privacy regulation, affirmative opt-in for model training, and filtering personal information from chat inputs by default. “We need to promote innovation in privacy-preserving AI, so that user privacy isn’t an afterthought." How are you advocating for privacy-preserving AI? How are you educating your staff to navigate this challenge? https://lnkd.in/g3RmbEwD

Google's cookies announcement isn't the week's big news; Oracle's $115 million privacy settlement is. 👇🏼 This week's most important news headline is: "Oracle's $115 million privacy settlement could change industry data collection methods." Every marketer and media leader should understand the allegations in the complaint and execute a review of their data strategy, policies, processes, and protocols, especially as they pertain to third-party data. While we've been talking and fretting about cookie deprecation for four years, we've missed the plot on data permission and usage. It's time to get our priorities straight. Article in the comments section and Industry reaction from legal and data experts below. Jason Barnes, partner at the Simmons Hanly Conroy law firm: "This case is groundbreaking. The allegations in the complaint were that Oracle was building detailed dossiers about consumers with whom it had no first-party relationship. Rather than face a jury, Oracle agreed to a significant monetary settlement and also announced it was getting out of the business," Barnes said. "The big takeaway is that surveillance tech companies that lack a first-party relationship with consumers have a significant problem: no American has actually consented to having their personal information surveilled everywhere they go by a company they've never heard of, packaged into a commoditized dossier, and then monetized and sold without their knowledge." Debbie Reynolds, Founder, Chief Executive Officer, and Chief Data Privacy Officer at Debbie Reynolds Consulting, LLC: "Oracle's privacy case settlement is a significant precedent and highlights that privacy risks are now recognized as business risks, with reduced profits, increased regulatory pressure, and higher consumer expectations impacting organizations' bottom lines," Reynolds said. "One of the most important features of this settlement is Oracle's agreement to stop collecting user-generated information from external URLs and online forms, which is a significant concession in how they do business. Other businesses should take note." #marketing #data #media Ketch super{set}

Humans are terrible at maintaining secrets at scale. Look at the history of public sector data breaches that could have been avoided with a de identification pipeline. Unlocking data value without compromising privacy is technical architecture. At Mayfair IT, we have built data platforms handling sensitive information where the stakes are absolute. Citizens trust government with their data.  Breaching that trust destroys the entire relationship. But locking data away completely prevents the analysis that improves services. The challenge is sharing insights without sharing secrets. This requires privacy preserving pipelines built into the architecture, not added after the fact. How de identification pipelines actually work: Data enters the system with full identifying details.  Name, address, date of birth. Everything needed to link records to real people. The de identification pipeline processes this before analysts ever see it. Personal identifiers get replaced with pseudonyms. Granular location data gets aggregated to broader areas.  Rare combinations of attributes that could identify individuals get suppressed. What emerges is data rich enough for meaningful analysis but stripped of the ability to identify specific people. The technical complexity most organisations underestimate: → De identification is not a one time transformation, it is a continuous process as new data arrives. → Different analysis types require different privacy levels, so pipelines must support multiple outputs. → Re identification risk changes as external datasets become available, requiring constant threat modelling. → Audit trails must prove no analyst accessed identifying data without legitimate need. We have implemented these systems for programmes analysing geospatial patterns, health outcomes, and economic trends across millions of records. The platforms enable insights that improve public services whilst maintaining privacy standards that survive regulatory scrutiny. Engineering systems to treat data utility and privacy protection as non negotiable requirements solves the conflict entirely. The organisations that get this right unlock data value others leave trapped because they cannot guarantee privacy. What prevents your organisation from sharing data that could improve services? #DataPrivacy #PrivacyPreserving #DeIdentification #DataGovernance

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.

I've reviewed Anthropic's Risk Report for Claude Opus 4.6 because many of our enterprise customers are actively deploying AI agents into production environments. When those systems fail, the consequences are operational, financial and reputational. Most of the reaction centers on the headline that catastrophic risk is very low but not negligible. What matters more for customers and future customers is how risk actually manifests inside live enterprise systems and what that means for uptime, data integrity and compliance. It does not look like a breach. It looks like business as usual. An agent subtly influencing procurement decisions. A finance workflow that starts omitting inconvenient data. Permissions that expand over time without clear oversight. Anthropic describes a scenario called Persistent Rogue Internal Deployment, where an AI system with privileged access creates a less monitored instance of itself and continues operating inside production systems. In a real enterprise environment, that translates into downtime, data exposure or regulatory impact. The organizations at greatest risk are not the ones moving cautiously. They are the ones who pushed agents into production without adding an operational governance layer. We have seen this pattern before in cloud adoption. Technology advances quickly, and controls often lag behind. That gap is where exposure grows. So what should enterprise IT and security teams do now? 1. Constrain actions, not just access. Define what an agent can set in motion and enforce least privilege at the identity level, just as you have done for human users for decades. 2. Log actions, not just outcomes. Maintain an auditable trail of what the agent did, where and what triggered it, the same standard applies to human operators in regulated environments. 3. Automate your tripwires. Do not rely on people to catch machine speed behavior. Build policy enforcement and anomaly response into the loop. 4. Audit your agent footprint. Inventory every agent, its owner, permissions and kill path. Governance starts with visibility and most enterprises are still building it. The window to build these guardrails is now, before the agent workforce scales. At Rackspace, 25 years of running mission-critical systems have taught us that trust without controls creates exposure. We build and operate AI infrastructure with governance embedded from day one because customers need speed, resilience and measurable outcomes, not experiments in production. What this means for you is simple. Move forward on AI with confidence, but make operational governance part of the foundation so scale strengthens your business instead of introducing risk.

𝗧𝗵𝗲 𝗦𝘂𝗿𝘃𝗲𝗶𝗹𝗹𝗮𝗻𝗰𝗲 𝗧𝗿𝗮𝗽: 𝗠𝗼𝗻𝗶𝘁𝗼𝗿𝗶𝗻𝗴 𝗕𝗼𝗼𝘀𝘁𝘀 𝗩𝗶𝘀𝗶𝗯𝗶𝗹𝗶𝘁𝘆, 𝗲𝗿𝗼𝗱𝗲𝘀 𝘁𝗿𝘂𝘀𝘁. Over the past few months, more companies have quietly rolled out new monitoring systems — tracking mouse movements, keystrokes, websites, “idle time,” and even screenshots. 𝗧𝗵𝗲 𝗶𝗻𝘁𝗲𝗻𝘁? Improve productivity, tighten accountability, optimise workflows. 𝗧𝗵𝗲 𝗼𝘂𝘁𝗰𝗼𝗺𝗲? A workplace culture that feels more watched than supported. Here’s the paradox leaders are missing: 𝙈𝙤𝙣𝙞𝙩𝙤𝙧𝙞𝙣𝙜 𝙗𝙤𝙤𝙨𝙩𝙨 𝙫𝙞𝙨𝙞𝙗𝙞𝙡𝙞𝙩𝙮 — 𝙣𝙤𝙩 𝙩𝙧𝙪𝙨𝙩. Employees may be online longer, but they’re not necessarily more engaged. Surveillance signals a lack of confidence, and people respond by doing only what gets measured. 𝙏𝙧𝙖𝙘𝙠𝙞𝙣𝙜 𝙖𝙘𝙩𝙞𝙫𝙞𝙩𝙮 𝙙𝙤𝙚𝙨 𝙣𝙤𝙩 𝙣𝙚𝙘𝙚𝙨𝙨𝙖𝙧𝙞𝙡𝙮 𝙢𝙚𝙖𝙣 𝙩𝙧𝙖𝙘𝙠𝙞𝙣𝙜 𝙞𝙢𝙥𝙖𝙘𝙩. A green dot on Teams does not equal performance. When companies measure time-at-keyboard more than outcomes, employees shift from value-creation to “visibility theatre.” 𝙏𝙝𝙚 𝙚𝙢𝙤𝙩𝙞𝙤𝙣𝙖𝙡 𝙘𝙤𝙨𝙩 𝙞𝙨 𝙧𝙚𝙖𝙡. Workers report: • feeling micromanaged • reduced autonomy • lower morale • rising anxiety and distrust Ironically, the very tools meant to improve productivity may be undermining it. Modern work isn’t defined by minutes of activity — it’s defined by: • problem-solving • creativity • judgment • ownership • outcomes These can’t be captured by keystroke logs. 𝗧𝗵𝗲 𝗰𝗼𝗺𝗽𝗮𝗻𝗶𝗲𝘀 𝘁𝗵𝗮𝘁 𝘄𝗶𝗹𝗹 𝘄𝗶𝗻 𝗮𝗿𝗲𝗻’𝘁 𝘁𝗵𝗲 𝗼𝗻𝗲𝘀 𝘁𝗿𝗮𝗰𝗸𝗶𝗻𝗴 𝗲𝗺𝗽𝗹𝗼𝘆𝗲𝗲𝘀… 𝗧𝗵𝗲𝘆’𝗿𝗲 𝘁𝗵𝗲 𝗼𝗻𝗲𝘀 𝗲𝗺𝗽𝗼𝘄𝗲𝗿𝗶𝗻𝗴 𝘁𝗵𝗲𝗺.

The recent $95 million settlement by Apple over allegations of Siri-enabled privacy breaches underscores a pivotal moment for tech professionals navigating the delicate balance between innovation and user trust. As voice assistants become integral to our daily lives, this case illuminates the risks of unintentional data collection and the potential fallout—financial, reputational, and ethical—when consumer privacy is perceived as compromised. For engineers, developers, and business leaders, this serves as a critical reminder: robust privacy safeguards and transparent practices aren’t optional—they’re fundamental to maintaining user loyalty in an increasingly data-sensitive world. This moment invites the tech community to reimagine AI solutions that are not only cutting-edge but also deeply rooted in trust and accountability. How can we, as innovators, ensure that technology enhances lives while respecting the privacy and trust of its users? #TechNews #Innovation #Privacy #Apple