Reclaiming Control: What AI Security Can Learn from Self-Custody

Series: Is AWS Bedrock (and Others) Safe Enough for Your AI Project? – Part 3 of 3

In Part 1, I examined how Big Tech’s AI platforms are scaling rapidly and why this creates lock-in risks for project teams.

In Part 2, I broke down the gap between security promises and project realities.

Now let’s imagine a different model.

What if AI were managed the way we manage assets in crypto—where ownership, not convenience, is the default?

Here’s what that mindset shift could mean for AI projects:

• 1️⃣ Bring Your Own Encryption. Client-side encryption before prompts or outputs ever touch a vendor API.
• 2️⃣ Portable Deployments. Models and fine-tunes that can move with you, not stay locked in the vendor’s vault.
• 3️⃣ Zero-Trust by Design. Assume no platform is fully secure. Red-team your stack, validate provenance, and treat portability as a security feature.
• 4️⃣ Transparent Control. Audit trails that can’t be altered, opt-out defaults instead of opt-in checkboxes, and explicit ownership of logs and weights.

Closing Thought:

AI projects today run on vendor trust. Tomorrow, they may need to run on self-custody principles—where teams hold the keys, set the rules, and keep the exit door open.

In the same way we demanded more control in finance, we’ll demand it in AI. And project managers will be the ones deciding whether their teams build with agility—or with handcuffs.

#AIProjectManagement #CloudAI #VendorLockIn #ResponsibleAI #ZeroTrust #TechLeadership #AIInfrastructure #CPMAI #FutureOfAI #CPMAI

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