27 New Roles in Privacy & AI for Q4

Privacy and data protection is changing fast. Half of this week's 37 new open roles have 'AI' in the title, and it's just getting started Corporate Counsel, Privacy @ H&R (Missouri) https://lnkd.in/geS25jjw Privacy Ops Program Mgr @ Stripe (Remote) https://lnkd.in/gK8sT9AH AI Governance Sr Assoc. @ PwC (NYC) https://lnkd.in/gT_bKahw AI Governance Spec @ IPTA (Huntsville, AL) https://lnkd.in/g8wbaKnV Compliance, Security & AI Governance @ Alliance (Remote) https://lnkd.in/g4fiAwk8 AI Governance Analyst @ Koniag Gov't Services (DC) https://lnkd.in/gjEtgZz8 Global Privacy & Records Process Analyst @ John Deere (Chicago) https://lnkd.in/g-YmnFc9 Analyst, Compliance & Privacy @ AMEX (NYC) https://lnkd.in/gtivrT8c Privacy Analyst, Response @ Ford (Dearborn, MI) https://lnkd.in/g4tybZ6q Privacy Sr Assoc. @ Crowe (Dallas, TX) https://lnkd.in/exAQ5fSf Legal Analyst, Data Privacy @ Sephora (SF) https://lnkd.in/gVZhSAg5 Privacy Counsel @ Yahoo (Remote) https://lnkd.in/gSQg366C Sr Counsel @ Higgsfield AI (SF) https://lnkd.in/g3HrDSdQ Privacy Counsel, Privacy Legal @ Amazon (NYC) https://lnkd.in/gREtCEVM AI & Data Governance Counsel @ GM (Remote) https://lnkd.in/gqMU7zFF Counsel, AI Risk & Compliance @ McDermott Will & Schulte (Chicago) https://lnkd.in/gVhrbqzu Research Eng, Privacy @ OpenAI (SF) https://lnkd.in/gVZgW2Fk Info Security Eng, AI & Data Privacy @ McKinstry (Seattle) https://lnkd.in/grBw6CY2 Software Eng, Data Privacy @ Temu (Remote) https://lnkd.in/gUN8yTjJ Trust Eng @ Harvey (SF) https://lnkd.in/gMuvv7yC Staff SWE, Privacy @ Anthropic (Seattle) https://lnkd.in/g6-nbF6j Head of Privacy, AI & Data Governance @ Takeda (Boston) https://lnkd.in/gd3mvGZB Privacy, Cybersecurity, & Emerging Techn Attorney @ Publix (Lakeland, FL) https://lnkd.in/gKE7mgVf Dir, Privacy @ Omada Health (Remote) https://lnkd.in/gT6ZJ2cK Dir, Data & AI Governance @ Cadent (NYC) https://lnkd.in/gWtmpSgb Responsible AI Program Lead @ Mercy (Chesterfield, MO) https://lnkd.in/gYrWJCra Principal Privacy Product Mgr, Health AI @ Microsoft (Redmond, WA) https://lnkd.in/gGE7SzTi International Dir, Enterprise Intelligence & Privacy @ POGO (Toronto) https://lnkd.in/gGYxBnAf Sr Consultant, Privacy & Data Protection @ KPMG (Toronto) https://lnkd.in/gfew_XdB Consultant, Privacy & AI @ Deloitte (Barcelona) https://lnkd.in/gtTQbSG5 Data Governance Expert @ Scopely (Barcelona) https://lnkd.in/gk_-KJZY Counsel, Privacy & AI Regulation (EU) @ Cohere (EU/Remote) https://lnkd.in/grDdsefA Privacy Counsel @ Index Exchange (London) https://lnkd.in/gUYfxYtt AI Privacy Eng Coord. @ Emagine (PT/Remote) https://lnkd.in/guDQffmP Technical Staff, Privacy Eng, Health @ Microsoft (London) https://lnkd.in/gr47QJhY Privacy & Responsible AI Mgr @ Vodafone (London) https://lnkd.in/gKhDJU5D Global Privacy Dir @ TaskUs (Athens) https://lnkd.in/gex73iDQ Repost so someone in your network finds the role they didn't know was open! If you're hiring, comment with the role and I'll feature it next week

Artificial intelligence-generated inferences will account for most privacy incidents by 2029, as AI systems increasingly derive sensitive personal information from seemingly innocuous data, Gartner said Thursday. The research and advisory firm said advances in generative AI and machine learning are enabling organizations and threat actors to infer sensitive attributesincluding health conditions, political views, financial status and behavioral patternsfrom anonymized, aggregated or other non-sensitive datasets, shifting privacy risks beyond the exposure of personally identifiable information. There is a fundamental shift underway from data exposure to insight exposure, Bart Willemsen, VP Analyst at Gartner, said in a statement. Organizations have historically focused on protecting raw personal data, but AI can now reconstruct deeply personal insights without ever breaching traditional data controls. Privacy risks are increasingly emerging from what AI algorithms infer about individuals rather than what data is directly exposed. Gartner said the shift requires organizations to expand privacy programs beyond traditional data protection and govern how AI systems generate, use and act on insights about individuals. The firm warned that organizations treating privacy solely as a data protection challenge will become increasingly vulnerable to inference-based privacy incidents. Inference attacks are particularly dangerous because they often evade conventional detection mechanisms, Willemsen said. Individuals can be exposed through AI-generated conclusions rather than leaked records, creating privacy risks that undermine data integrity and are difficult to detect, explain and mitigate. To reduce those risks, Gartner recommended embedding AI governance into privacy programs, adopting privacy-enhancing technologies such as differential privacy and synthetic data, and strengthening data minimization and lifecycle controls to limit unnecessary data retention. #Gartner #AIPrivacy #AIGovernance #DataPrivacy #ArtificialIntelligence #ResponsibleAI #EnterpriseAI #Privacy #CyberSecurity #NervNow

AI Privacy Risk Is Moving Beyond Data Exposure Gartner predicts that by 2029, most privacy incidents will stem from AI-generated inferences about people rather than the direct exposure of personally identifiable information. The distinction matters. AI can draw conclusions about health, behaviour, vulnerability or other sensitive attributes from data that may appear innocuous, aggregated or anonymised. This sharpens a practical governance question for company boards and executives: What conclusions are our AI systems permitted to draw about people, and what happens when those conclusions influence a decision? Considerations companies could be making include: Decision boundaries: define which inferences are permitted, prohibited or subject to additional approval before deployment. Authority: identify who approves a sensitive inference use, who can stop it and who remains accountable when it affects a customer, employee or other person. Assurance: test for unintended inferences, bias, overfitting and circumstances in which apparently low-risk data can reveal sensitive attributes. Change control: reassess inference risks when models, prompts, data sources, integrations or business purposes change. Evidence: retain the purpose, testing, exceptions, human review requirements and accountable approvals supporting each material inference use. Protecting the source data remains essential but a company may also need to govern the new information an AI system creates from that data and the decisions made from it. The question this creates is: Can we show what sensitive conclusions our AI systems can draw about people, which uses are authorised and who is accountable before those conclusions influence a decision? The strategic implication is clear: Effective AI privacy governance must connect data protection, inference boundaries, human decision authority, lifecycle assurance and evidence of control operation. https://lnkd.in/guPCVw8a #AIGovernance #PrivacyGovernance #BoardGovernance #AIAssurance #DataGovernance #ResponsibleAI #RiskManagement #AustralianBoards #ArtificialIntelligence #CorporateGovernance

Strip the name, the email and the ID from a dataset, and it looks safe. Looking safe and being safe are not the same thing. In regulated organizations, the detail that identifies a person is rarely the obvious identifier. It's the rare combination of attributes that, taken together, still points back to one individual. Remove the direct fields and that combination often survives untouched. Data that passed a checklist can still be pieced back together, and teams usually learn this only after the data has already moved. That is the quiet flaw in most "de-identified" data: privacy was assumed, not tested. PAMOLA, Realm's Privacy Engineering Studio, is built for exactly that gap. It produces provably private, AI-ready data that stays useful, delivered through a reusable, policy-enforced pipeline: configured once, with new data flowing through the same governed stages. Two things make the approach different. First, it doesn't stop once privacy techniques are applied. After data is protected, PAMOLA runs simulated privacy attacks against its own output, and only data that holds is released. "Provably private" means something specific here: not "we applied privacy methods," but "we attacked the result under stated assumptions and it held." Second, protection and usefulness are treated as one goal, not a trade-off. Data that is safe but unusable unblocks nothing. PAMOLA is built to keep data useful for AI, analytics, model development and testing while holding privacy under adversarial pressure. Each run produces a PAMOLA Evidence Pack: a structured account of what was done and how the protection held, one a reviewer can re-derive independently and hand to an auditor. For a data leader whose AI project is frozen waiting on data they're allowed to use, or a privacy officer who needs evidence rather than assurances before signing off, that shifts the default answer from "no" to "yes, with controls." PAMOLA is one surface of Realm, the governed depth layer beneath enterprise AI: it governs the data before AI uses it, inside the organization's own perimeter. Sensitive data doesn't have to stay frozen to stay protected. See how PAMOLA turns sensitive data into data your teams can actually use https://lnkd.in/g72Ccadi #RegulatedAI #DataPrivacy #PrivacyEngineering #AIGovernance #EnterpriseAI

Most professionals wont take AI governance seriously until they have to. By then, theyll already be behind: 3 reasons you cant afford to wait 1. AI is moving faster than most organisations can govern it. Every week brings another AI tool, another use case, another regulatory development. The real risk isnt falling behind. Its making decisions without understanding the governance implicationsand only discovering the consequences when its too late. 2. No one will lower their expectations because the legislation is complicated. Sooner or later, someone senior will ask: Can we use this AI tool? What are the risks? What does the EU AI Act actually require? In that moment, youll either be the person with the answer or the person scrambling for one. 3. The professionals learning this today will become the trusted advisers of tomorrow. AI governance is rapidly becoming part of Governance, Risk, Compliance, Privacy, Legal and Security roles. Those who understand it early wont just reduce risk. Theyll become the people organisations trust. The professionals clients seek out. The experts employers want to hire. Thats why I recommend The Easy Peasy Guide to the EU AI Act by Jamal Ahmed. Before it was even released, it was outselling John Grisham on Amazon. Since launch 3 days ago, it has become an Amazon International Bestseller, reached Bestseller status in five countries, and now holds the #1, #2 and #3 bestselling positions in its category. Why? Because it explains one of the worlds most important AI regulations in plain English, with practical examples you can immediately apply. If AI is becoming part of your role, and it is,this is one of the smartest investments you can make in your professional credibility. #AIGovernance #EUAIAct #Governance #RiskManagement #Compliance #Privacy #ArtificialIntelligence Get your copy: https://amzn.eu/d/0hTG14K9

Privacy risks are increasingly emerging from what AI algorithms infer about individuals rather than what data is directly exposed. Full story https://buff.ly/7ZzwVIl #Tech | #News | #AI | #Privacy

145 AI laws in one year and your privacy team just got smaller. The DataGrail Privacy and AI Trends Report 2026 shows: 145 AI-related laws enacted in 2025 in the U.S. 63.6% of AI vendors hiding subprocessors your program needs to know Privacy headcount down as much as 33% Manual data subject requests costing about $1.5M/year for mid-sized firms 42% of organizations abandoning AI projects due to privacy hurdles The message is clear: compliance teams must do more with less and faster. Read the full report to recalibrate your AI deployment posture and governance. #Privacy #AI #Compliance #Sponsored #DataProtection #Governance #DataPrivacy #RegTech #AIRegulation #PrivacyTech #TechPolicy #Kiteworks

AI is making it faster and easier to infer sensitive personal information from ordinary business data. Gartner's Bart Willemsen explains why, and what CISOs need to do about it https://lnkd.in/gMrsPVik

From concerns regarding data privacy to ethical considerations, lets explore a comprehensive array of reasons why businesses need to exercise caution when employing AI for text generation. 1. Data Privacy and Security Risks AI text generation often...

How to Reduce the Privacy Impact of Shadow AIUse Shadow AI presents a silent but severe risk to corporate data. Learn how to identify unauthorized AI tools and implement a framework to reduce the privacy impact of shadow AI....