AI Security Promises vs. Project Realities 🧠 Series: Is AWS Bedrock (and Others) Safe Enough for Your AI Project? – Part 2 of 3

In Part 1, I talked about how Big Tech’s AI platforms are scaling fast—and how vendor lock-in creates hidden risks for project teams. Now let’s look at the other side of the equation: security.

On paper, cloud AI platforms sound bulletproof.

AWS Bedrock encrypts data at rest. Azure OpenAI inherits Microsoft’s long list of compliance badges. Salesforce wraps AI in the same governance controls that made it a CRM powerhouse.

But for project managers delivering real initiatives, here’s where theory collides with practice:

• 1️⃣ Shared responsibility isn’t simple. Vendors secure the infrastructure. You secure the data, prompts, and access. That blurry line often shows up in audits, not dashboards.
• 2️⃣ Default logging creates exposure. Most platforms capture prompts and outputs by default. Sensitive information may persist longer than your team expects.
• 3️⃣ Fine-tuning doesn’t travel. A model tuned in one cloud usually stays there. Portability is limited, which makes multi-cloud strategies much harder than they look in a pitch deck.

For AI project managers, these aren’t abstract technical notes—they’re project risks that shape cost, compliance, and long-term flexibility.

Closing Thought:

Cloud AI platforms are “secure enough” for enterprises. But they’re not designed for ownership. That mindset—the idea that you hold the keys, not the vendor—is missing.

👉 In Part 3, I’ll explore what AI security could look like if we borrowed principles from a different world, one that treats self-custody as the default.

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