Implementing AI 101: Security, Privacy & Responsibilities You Need to Consider
oints down into readable, actionable insights. Which will enable you to set the standards needed to lock down your AI initiatives.
Security
Since any AI initiatives run on your most sensitive data and a combination of cloud resources, anything misconfigured can be an attackers golden ticket into your "chocolate" factory.
You should be implementing zero trust architecture (ZTA) as standard. That means:
Verifying each request
Segmenting your network.
Encrypting your... everything at rest and in transit.
You also need to kill the "shared admin credential" culture and implement short-lived credentials like SAS tokens, service identities and even managed identities so that resources talk to each other without needing Dave from ITs Global Admin Access.
Privacy
Data privacy is not just a requirement but it's also about respecting the human behind the data. Regardless of your position, shopping habits or general browsing everyone wants the same thing, not to have their data misused.
Putting the correct processes in place means consent and data minimisation is automated. Build processes that refuse data when consent is missing (I know a lot of leaders won't be happy with this but its true). Make sure you're anonymising PII data or tokenising it. Any raw PII data should be siloed in a very secure zone.
Put it this way, violations of privacy laws already carry eight-figure fines. It's really not worth the risk. Especially with the EU AI Act already in force, which makes privacy checks part of the model approval pipeline, so you're going to be held accountable either way.
Responsible (AI)
Responsible AI is really just about making sure that your AI systems are staying inline with key areas:
Fairness: Detect and fix your bias.
Transparency: Explain why and how it works.
Privacy/Security: Protect data, mitigate attacks.
Accountability: Clear ownership and always have human oversight
This means making sure that you're weaving in these practices into your pipelines and services, not just adding it on at the end. Examples of this:
Bias tests in CI/CD: fail the build if statistical parity or equal-opportunity thresholds slip.
Model cards & lineage: So auditors can retrace every feature and decision path.
Ethics register & human sign-off: For high-risk use-cases (credit, hiring, health).
Again, this is going to mandatory if it isn't already and a further point. Is if anything like this crops up in your systems/services the social backlash would kill your product faster than any bug could.
TL;DR
Zero-Trust Everywhere: Encrypt, segment, and verify every request, no implicit trust.
Consent-First Data: Collect only what’s needed; strip or tokenise PII at ingest.
Bias Gates in CI/CD: Fail the build if fairness metrics fall short.
Metadata Guardrails: Auto-mask and govern data via catalog-driven policies.
Always-On Auditing: Log and review security, privacy, and model behaviour 24 / 7.
Want to Learn More About AI-Readiness?
I've recently released an eBook that covers AI-readiness across the data engineering function. However, a lot of the information covers best practice, key frameworks and operational culture shifts needed.
https://jonathonkindred.gumroad.com/l/data-engineering-for-ai-readiness
Let me know what you think via comment, DM or a review!