Agree & Join LinkedIn

By clicking Continue to join or sign in, you agree to LinkedIn’s User Agreement, Privacy Policy, and Cookie Policy.

Skip to main content
LinkedIn
  • Articles
  • People
  • Learning
  • Jobs
  • Games
Join now Sign in
Last updated on Mar 30, 2025
  1. All
  2. Engineering
  3. Artificial Intelligence (AI)

You're developing AI models for sensitive industries. How do you ensure data privacy?

When developing AI models for sensitive industries, it's crucial to implement robust data privacy measures. Here's how you can ensure data privacy:

  • Encrypt data: Use advanced encryption methods to protect data both in transit and at rest.

  • Implement access controls: Restrict data access to only those who need it, using role-based access controls.

  • Regular audits: Conduct frequent privacy audits to identify and address vulnerabilities.

How do you ensure data privacy in your AI projects? Share your strategies.

Artificial Intelligence Artificial Intelligence

Artificial Intelligence

+ Follow
Last updated on Mar 30, 2025
  1. All
  2. Engineering
  3. Artificial Intelligence (AI)

You're developing AI models for sensitive industries. How do you ensure data privacy?

When developing AI models for sensitive industries, it's crucial to implement robust data privacy measures. Here's how you can ensure data privacy:

  • Encrypt data: Use advanced encryption methods to protect data both in transit and at rest.

  • Implement access controls: Restrict data access to only those who need it, using role-based access controls.

  • Regular audits: Conduct frequent privacy audits to identify and address vulnerabilities.

How do you ensure data privacy in your AI projects? Share your strategies.

Add your perspective
Help others by sharing more (125 characters min.)
23 answers
  • Contributor profile photo
    Contributor profile photo
    Vivek Gupta

    Top AI Voice | Patent Filed: AI Grant Assistant | Founder & CEO | Digital transformation expert | Author and keynote speaker

    • Report contribution

    To maintain data privacy in AI development across sensitive sectors, we focus on a human-centric strategy. We use strong encryption and anonymization methods to hide user identities. Regular audits and robust access controls make sure that only approved staff handles sensitive information. We also follow a human-in-the-loop model, where human intervention is central to the decision process, particularly in high-risk situations. By promoting feedback from users, we make AI interactions more in tune with human values. It not only protects privacy but also builds trust and empathy towards AI, leading to a responsible and responsive environment.

    Like
    8
  • Contributor profile photo
    Contributor profile photo
    Isha Taneja

    Driving awareness for Data & AI-powered strategies || Co-Founder & CEO @Complere Infosystem || Editor @The Executive Outlook || Chair @TIE Women Chandigarh || Host@The Executive Outlook Podcast

    • Report contribution

    Building AI for sensitive stuff? You gotta lock it down from the jump. Here’s how to keep data safe and sound: 1. Encrypt it all Moving or stored — scramble it so no one can snoop. 2. Control the keys Only the right folks get in. Roles matter. 3. Audit on repeat Keep checking, keep fixing. Don’t wait for a leak to act. Privacy ain't optional — it's part of the build. How are you keeping your AI airtight?

    Like
    6
  • Contributor profile photo
    Contributor profile photo
    Bala J

    AI & Digital Transformation Leader | Generative AI |TOP AI voice|TOP DataGovernance voice |Keynote Speaker&Mentor | Innovation |Chief Data Scientist | Enterprise & Analytics Architect | LLM |Azure AWS & GCP | RPA

    • Report contribution

    “With great data comes great responsibility.” – Sam • Encrypt data – Apply end-to-end encryption to secure sensitive information. • Control access – Use strict role-based permissions to minimize exposure. • Conduct audits – Perform regular privacy and security assessments. • Anonymize & tokenize – Remove or mask personally identifiable information. • Follow regulations – Ensure compliance with GDPR, HIPAA, and industry standards.

    Like
    6
  • Contributor profile photo
    Contributor profile photo
    Devesh Bissa

    Staff Software Engineer @ Walmart | Computer Vision | Chat bot | TOGAF - SimpliLearn | SAFE 5 PO/PM

    • Report contribution

    Addition to what already suggested about securing data on rest, on transit. There needs a mock replication of original data which is real like data but not belongs to real world entity. It's complicated but if achieved than can be used for preparing model with more accuracy. In AI use cases data is critical, preparing mock replication of original adding lenght of work.

    Like
    4
  • Contributor profile photo
    Contributor profile photo
    Harry Waldron, CPCU

    Business Systems Analyst

    (edited)
    • Report contribution

    Actually, every organization has "SENSITIVE" data. However, sectors like GOVT, banking, insurance, health care, etc. have highly confidential data to protect with MAX standards available. The recent SIGNAL security failure shares a need to go beyond just locking down technically. There is a HUMAN behavioral side where every user must follow best practices. The "fox can get into the henhouse" more often by user mistakes than the bad guys hammering technologically SECURITY & PRIVACY must be planned, evaluated, and maximized from start to finish. There must be ZERO compromise for AUDIT/LEGAL standards & corporate security policies PRIVACY LEGAL NEEDS * SAS-70 * GDPR * CCPA * HIPAA * SOX/COSO/COBIT * PCI-DSS * SOC2 * NIST * ISO 27001

    Like
    3
  • Contributor profile photo
    Contributor profile photo
    Max K.

    Chief Business Development Officer | Integrating AI solutions to startups and scale-ups to optimize their operations

    • Report contribution

    If you’re building AI for sensitive industries and not thinking about privacy from day one, you’re already behind. In my experience, privacy isn’t just about compliance — it has to be built in from the start. We follow a privacy-by-design approach: encryption, anonymization, and strict access controls are standard. Regular audits and close monitoring help us stay ahead as systems scale. Human-in-the-loop has also been key. In high-risk areas, you need that human layer of judgment and accountability. And transparency? Non-negotiable. Users don’t need every detail, but they do need to trust the system. No trust, no adoption. Protecting data means protecting people — and that’s what responsible AI is all about.

    Like
    3
  • Contributor profile photo
    Contributor profile photo
    Hemant Phalak

    Co-Founder - Entvin: Regulatory AI Agents | Y-Combinator | IIT Bombay

    • Report contribution

    In AI projects for sensitive industries, data privacy is a top priority. Strong encryption methods are essential, ensuring that data is protected both during transit and when stored. Access controls play a critical role, limiting data visibility to only authorized personnel based on their roles. Additionally, integrating privacy by design into the development process ensures that data protection is considered at every stage, fostering trust and compliance throughout the project.

    Like
    2
  • Contributor profile photo
    Contributor profile photo
    Ali Abdollahi

    Application and Offensive Security Manager | Microsoft MVP | Speaker | Author

    • Report contribution

    I ensure data privacy by using a multi-layered strategy: encrypting data at rest and in transit, applying anonymization and differential privacy during model training, enforcing strict role-based access control and multi-factor authentication, and setup periodic security audits to maintain regulatory compliance. This can be done through penetration testing on the models, vulnerability assessment, and compliance checks with industry standards like GDPR, HIPAA, PCI-DSS, etc.

    Like
    2
  • Contributor profile photo
    Contributor profile photo
    Terence J. Fitzpatrick

    AI & Generative AI Leader | CEO & Global CRO | AI-Driven Business Growth | Computer Vision & Blockchain Innovator | AI Strategy & Revenue Acceleration

    • Report contribution

    When developing AI for sensitive industries, I build privacy into the design from day one. That means using anonymized datasets, limiting access, and applying strict compliance frameworks. But beyond tools and policies, I focus on transparency—clearly communicating how data is used and protected. Privacy isn’t just a technical issue, it’s a trust issue—and trust is earned through responsible design and clear accountability.

    Like
    1
  • Contributor profile photo
    Contributor profile photo
    Robert Wohleber, CFA

    🔄 Bridging Finance & AI | Cybersecurity & Risk in Capital Markets | Innovation & Digital Strategy

    • Report contribution

    🔐 Privacy by Design Isn’t Optional—It’s the Foundation When working with AI in sensitive industries, I approach privacy like a structural engineer would approach load-bearing design: built-in from the blueprint, not bolted on later. Here’s what matters most: • 🔁 Data minimization—only collect what’s needed, and keep it lean. • 🧱 Privacy architecture—role-based access, strong encryption, and audit trails should be embedded from day one. • 🧠 Human oversight—AI may process the data, but humans must guard the gates. Ultimately, it’s not just about protecting data—it’s about protecting trust. And in sectors like finance or healthcare, trust is currency. #AIPrivacy #ResponsibleAI #AIinFinance #DataGovernance #AICompliance

    Like
    1
View more answers
Artificial Intelligence Artificial Intelligence

Artificial Intelligence

+ Follow

Rate this article

We created this article with the help of AI. What do you think of it?
It’s great It’s not so great

Thanks for your feedback

Your feedback is private. Like or react to bring the conversation to your network.

Tell us more

Report this article

More articles on Artificial Intelligence

No more previous content
  • You're developing AI-driven applications with sensitive user data. How can you ensure its protection?

    9 contributions

  • You're facing stakeholder concerns about AI risks. How can you still push for innovation?

    17 contributions

  • You're facing privacy concerns with AI technology. How can you protect user data effectively?

  • You're leading an AI project with stakeholders. How do you convince them of the importance of data privacy?

  • You're leading an AI project with stakeholders. How do you convince them of the importance of data privacy?

    47 contributions

  • You're tasked with securing sensitive information in AI models. How do you tackle data privacy risks?

    29 contributions

  • Your team is struggling with understanding AI data privacy. How can you effectively educate them?

    33 contributions

  • How would you address bias that emerges from unintended consequences in AI algorithms during testing phases?

    46 contributions

  • Your team has varying levels of AI knowledge. How can you ensure everyone is on the same page?

    105 contributions

No more next content
See all

More relevant reading

  • Artificial Intelligence
    What are the most important considerations for facial recognition technology in computer vision?
  • Artificial Intelligence
    How do you secure AI performance?
  • Computer Engineering
    What are the best practices for securing AI training data?
  • Artificial Intelligence
    How can you secure AI models during deployment and monitoring?

Explore Other Skills

  • Programming
  • Web Development
  • Agile Methodologies
  • Machine Learning
  • Software Development
  • Computer Science
  • Data Engineering
  • Data Analytics
  • Data Science
  • Cloud Computing

Are you sure you want to delete your contribution?

Are you sure you want to delete your reply?

  • LinkedIn © 2025
  • About
  • Accessibility
  • User Agreement
  • Privacy Policy
  • Cookie Policy
  • Copyright Policy
  • Brand Policy
  • Guest Controls
  • Community Guidelines
Like
23 Contributions