By Ravi Srivatsav, Co-Founder and CEO, DataKrypto  

The first phase of enterprise AI was about access: models, GPUs, cloud infrastructure, copilots, agents, and APIs. The next phase will be about proof.

Can an enterprise prove that sensitive data, proprietary models, prompts, embeddings, outputs, and AI workflows remain protected while AI is actually running?

That question now sits at the center of enterprise demand, regulatory direction, and national security.

Two Policies. One Conclusion.

The EU AI Act is pushing high-risk AI toward stronger data governance, cybersecurity, robustness, documentation, and accountability.

The new White House AI security Executive Order mandates the federal government to harden its own systems against AI-enabled threats, on fixed deadlines. And it directs CISA to guide and equip private critical infrastructure to do the same, alongside secure frontier-model deployment and protection of American IP. 

Different policy models. Same conclusion.

AI is becoming critical infrastructure. And critical infrastructure cannot depend on trust assumptions alone.

The Question Has Changed

For CIOs and CISOs, the hard question is no longer Can we adopt AI? It is: Can we safely use the data that makes AI valuable?

Customer records. Clinical data. Financial data. Cyber telemetry. Source code. Sovereign data. Regulated datasets. Model weights. Private prompts and embeddings.

That is where today’s security model starts to break.

The Cleartext Gap

Encryption at rest and encryption in transit were built for the cloud era.

AI creates a harder problem: exposure during computation.

During inference, RAG, fine-tuning, training, agent workflows, and model serving, sensitive data and model IP can move through application layers, tokenizers, memory, buses, accelerators, orchestration tools, and third-party infrastructure.

That is the cleartext gap.

At DataKrypto, our view is simple: the AI application layer should not be treated as a safe boundary for sensitive data.

Not because every system is malicious. But because modern AI stacks are too distributed, too dependent on third-party components, and too fast-moving to assume every layer can always be trusted.

The Next Category: Verifiable Confidential AI

The answer is not just more monitoring. It is not just better access control. It is not confidential computing alone.

The next category is verifiable confidential AI.

That means two things must hold together:

1. The environment must prove what it is before keys are released.

2. The data and model must remain protected while computation happens.

This is the shift from asserted trust to cryptographic evidence. From “trust the infrastructure” to “verify the workload.” From “secure the system” to “remove the data exposure.”

What We’re Building

That is what we are building with FHEnom for AI™ at DataKrypto: encrypted execution for AI workloads and a verifiable trust path from silicon to application.

With our integration of Intel® Trust Authority, FHEnom for AI can verify the Intel® TDX hardware platform, guest operating system, and FHEnom application identity before a wrapped session key is released.

Read more about our Intel® Trust Authority integration →

What This Means for Enterprise Buyers

For enterprise buyers, this changes the AI adoption curve:

• CIOs can move from pilots on sanitized data to production AI on real enterprise data.
• CISOs get a stronger control layer for workloads where plaintext exposure is unacceptable.
• Regulated industries get a more defensible foundation for privacy, governance, sovereignty, and auditability.

And the broader AI infrastructure ecosystem gets a clear signal: the next generation of AI platforms will require more than compute. They will require a trusted execution foundation across silicon, GPU infrastructure, cloud, neocloud capacity, key management, attestation, and encrypted AI execution.

The Real AI Race

The AI race will not be won only by whoever has the largest model. Or the most GPUs. Or the biggest cloud footprint.

It will be won by whoever can safely put their most sensitive data to work.

That is the market DataKrypto is building for.