Plausible Deniability: Architecting Covert Decoys for Vulnerable Users
Standard privacy measures fail when the threat actor has physical access to the device. Here is how I use AI generation to build immersive decoy architectures.
In enterprise security, we talk about Zero Trust and end-to-end encryption. But for survivors of domestic abuse, the threat isn't a hacker intercepting a packet - it's an abuser standing behind them demanding to see their phone.
Existing 'covert' apps often disguise themselves as calculators or weather apps. Abusers know this. Furthermore, if a victim is frantically typing an evidence log into a 'weather app,' it immediately raises suspicion. You need a reason to be typing.
For Project Dana, I architected an entirely functional AI Meal Planner. The user has a valid, verifiable reason to be typing paragraphs of text. Specific, innocuous search terms unlock the encrypted gratitude journal to log love-bombing, while other terms unlock the immutable database for logging physical abuse.
True security in these environments isn't just about data encryption; it is about social camouflage. The application must provide absolute plausible deniability.
Project Dana
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