Our Approach to Data and AI Security

At Anthralytic, we believe AI can be transformational for social impact—but only when implemented securely.

That begins with a solid foundation: understanding where your data lives, how it flows, and what’s at stake if it’s mishandled. Our approach centers on practical, human-centered safeguards rooted in cybersecurity best practices and adapted for the real risks of social impact work.



Mitigating Data Vulnerabilities in Social Impact and AI

We prioritize four key actions before AI ever enters the picture:

  • Begin with a Data Map: Understand what you’re collecting, where it’s stored, who owns it, and how sensitive it is.
  • Apply Strong Governance and Encryption: Encrypt sensitive data, restrict access, and set clear retention policies.
  • Build AI-Specific Safeguards: Use private, secure deployments. Never send sensitive data into public models unless fully anonymized.
  • Educate Continuously: Train your team regularly. Risks evolve—and so should your knowledge.



Core Principles of Cyber and Data Security

We apply established cybersecurity principles—not as a checklist, but as real-world ethical safeguards:


  • Confidentiality: Restrict access to authorized individuals only
  • Integrity: Maintain accuracy and reliability
  • Availability: Ensure data and systems are accessible when needed
  • Authentication: Verifying that users are who they say they are before granting access.
  • Non-Repudiation: Keeping secure logs to ensure users can’t deny their actions later.
  • Least Privilege: Giving people only the minimum access they need—no more.
  • Defense in Depth: Layering multiple safeguards so one failure doesn’t expose the system.
  • Separation of Duties: Splitting responsibilities across people or teams to reduce misuse.
  • Security Awareness Training: Teaching everyone—not just IT—how to spot risks and act safely.
  • Incident Response: Having a plan to respond quickly and effectively if something goes wrong.
  • Data Backup and Recovery: Making sure data can be restored if it’s lost, damaged, or stolen.



Adapted Security Principles for Social Impact

In our sector, the stakes are higher. The data we work with represents real people in vulnerable contexts:

  • Do No Harm: Prioritize safety and privacy.
  • Rigorous Data Minimization: Collect only what’s essential.
  • Human-Centered Risk Assessment: Focus on real-world consequences.
  • Inclusive, Accessible Security: Build tools everyone can use—especially non-technical staff and marginalized groups.
  • Trust and Transparency: Give communities meaningful control over their data.



AI-Specific Risks and Principles

AI introduces new risks. We address them with clear principles:

  • Build Internal Literacy – Train your team. Don’t outsource judgment.
  • Audit for Equity – Challenge bias in inputs and outputs.
  • Demand Explainability – No black boxes for high-stakes use.
  • Plan for Adversaries – Prepare for attacks like data poisoning.
  • Monitor and Recalibrate – Evaluate regularly. Retrain as needed.
  • Ground AI in Reality – Validate with evidence. Keep humans in the loop.
  • Fix the Pipeline First – Secure your inputs before you automate.
  • Operate Above the Minimum – Don’t wait for regulation to do what’s right.



Our Bottom Line

AI won’t strengthen your program if your data foundation is unstable. We help you shore up the basics first—because secure, ethical, human-centered data practices are the only way to ensure AI works in service of your mission, not against it.



Want help implementing secure, socially responsible AI and data practices? Contact us for more information.