#Privacy

Articles about Privacy — exploring patterns, best practices, and real-world implementations in production systems.

5 posts tagged with privacy. ← All posts

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Pratik Dhanave · ·6 min read

Privacy and Local-First Design

On-device AI's biggest promise is privacy — but that promise is only real if the architecture actually keeps data on the device. Privacy isn't a feature you add; it's a property of a design where sensitive data has no path off the phone. This post is about building that property in deliberately, and about the honest hybrid designs for when pure local isn't enough.

On-device AI's biggest promise is privacy — but only if the architecture actually keeps data on the device. Privacy isn't a feature you add; it's a property of a design where sensitive data has no path off the phone.

#On-Device AI#Privacy#Edge AI#Software Architecture#Flutter
Pratik Dhanave · ·7 min read

Data Privacy and Protection

Compliance requires you to collect and keep a great deal of personal data; privacy law requires you to protect it, limit it, and sometimes delete it. Data privacy is the compliance domain that governs the data all the other controls depend on — and it turns "how you handle personal information" from a courtesy into a set of enforceable engineering obligations.

Compliance requires you to collect and keep a great deal of personal data; privacy law requires you to protect it, limit it, and sometimes delete it. Data privacy governs the data all the other controls depend on — turning data handling into enforceable engineering obligations.

#RegTech#Compliance#Privacy#Backend Engineering#Fintech#Governance
Pratik Dhanave · ·13 min read

Data and Model Attacks

A defender's tour of the attacks that target the model and its data — prompt and context extraction, training-data memorization, membership inference and model inversion, model stealing, poisoning and backdoors, and evasion — with what a red-teamer tests and what actually stops each one.

Attacks on the model and its data: system-prompt and training-data extraction, membership inference and model inversion, model stealing, and poisoning/backdoors — what to test and defend, and the honest risk difference between using hosted models and training your own.

#AI Red Teaming#Privacy#AI Security#Model Security#Security#Prompt Engineering#Testing
Pratik Dhanave · ·13 min read

Data Security and Privacy

Part three of the AI Security Engineering series: protecting the data that flows through an LLM system — how sensitive information leaks out of prompts, logs, and retrieval, and the engineering controls (redaction, data minimization, per-user retrieval authz, residency choices) that actually stop it.

Protecting data in LLM systems: sensitive-information disclosure, PII in prompts and logs (your observability can be the leak), redaction with Presidio, data residency, and per-user access control on the retrieval layer so RAG doesn't leak across tenants.

#AI Security#Privacy#PII#Security#RAG#LLMs#Prompt Engineering#Observability
Pratik Dhanave · ·8 min read

Privacy, Compliance, and When to Get a Lawyer

Handling user data used to be a technical matter; now it's a legal one, with real regulations, real penalties, and real obligations that engineers build software to satisfy. Privacy and compliance have become part of the job — and, along with the rest of this series' legal basics, they lead to the single most important lesson: legal literacy exists to tell you when you're out of your depth and need a real lawyer. This closing post covers privacy, compliance, and that essential meta-skill. (Educational, not legal advice.)

Handling user data used to be a technical matter; now it's a legal one, with real regulations, penalties, and obligations. Privacy and compliance are part of the job — and they lead to the most important lesson: legal literacy exists to tell you when you're out of your depth and need a real lawyer. (Educational, not legal advice.)

#Legal#Privacy#Compliance#Startups#Intellectual Property#Governance

All posts on this site are written by Pratik Dhanave, an Agentic AI Architect with 7+ years building production distributed systems, multi-agent AI platforms, and cloud-native infrastructure. About the author → Each article includes working code, architecture diagrams, and references to the specific frameworks and standards discussed. Browse all posts or explore related topics using the tag cloud above.