Post-Quantum Cryptography & AI Safety
Post-quantum security.
Private AI research.
We help organizations prepare their encryption for quantum computers. We also research how Fully Homomorphic Encryption can let people use cloud AI without exposing their questions and documents to its provider.
Services: cryptographic assessments and migration planning.
Research: private AI use and independent model audits.
How encrypted AI would work
Fully Homomorphic Encryption
“Help me test this unpublished idea.”
- 1You encrypt the question.
Your device keeps the decryption key.
- 2Cloud AI computes on encrypted data.
The provider runs the computation without receiving a readable copy of your question.
- 3You decrypt the answer.
The result returns encrypted. You decide who can read it.
Research direction: a private AI service using a model adapted for encrypted computation.
Why this is AI Safety
Using AI should not require exposing your private thoughts.
AI safety includes protecting people from misuse of AI and from misuse of their data by the organizations that run it.
Our focus is concrete: protect the contents of AI conversations, let people collaborate without giving one provider all their information, and give independent auditors evidence of how a model behaved.
Protect private ideas.
A researcher may need AI to explore an idea that an employer, platform, or government would oppose. Fully Homomorphic Encryption can keep the question and answer encrypted during cloud computation.
The safety benefit: reduce the information an operator could use to profile, pressure, or target the person developing that idea.
Read the privacy argument ↗Share AI, keep control of data.
Several organizations could use shared cloud computing while their inputs remain encrypted. Our proposed design would specify which participants can approve decryption and read each result.
The safety benefit: people can work together without giving a single infrastructure owner a readable copy of everyone’s contributions.
Read the collaboration proposal ↗Check more than the answer.
We propose an independent recorder that captures selected internal model calculations as encrypted evidence. An authorized auditor could decrypt those records and compare them with the model’s behavior.
The safety benefit: investigators get measurements to examine alongside the model’s own explanation. Verification would tie those records to the actual computation.
Read the audit proposal ↗Our position: privacy protects the space in which new ideas develop. Independent scrutiny helps people examine powerful AI systems. Together, these are practical ways to limit concentrated power over human thought.
Research example: encrypted language-model computation ↗Our work
What we do.
We provide post-quantum security services and publish research proposals for private AI use and independent auditing.
Plan your move to
post-quantum security.
We help organizations understand their cryptographic dependencies, prioritize long-lived information, and plan a controlled transition to post-quantum algorithms.
Assess & discoverInventory algorithms, certificates, keys, and critical data flows.
Plan & prioritizeMap data lifetimes, vendor dependencies, and migration options.
Implement & validateEvaluate standards-based algorithms and hybrid deployments; test interoperability and performance.
Manage & evolveMaintain key governance, implementation review, and the ability to replace algorithms as evidence changes.
Explore your migration timing
Stored encrypted traffic can remain exposed to future cryptanalysis. Compare your data lifetime and migration time with an assumed quantum horizon. Use this planning scenario to test assumptions and prioritize migration work.
Lifetime + migration = 13 years. This exceeds the assumed horizon by 3 years.
NIST migration resources ↗Research private AI
and independent audits.
Our current work is a set of published arguments and experiment designs. We use Fully Homomorphic Encryption as a starting point for three questions that can be investigated directly.
Measure answer quality, processing time, memory, and what the provider could still infer from request sizes or timing.
Can independent records improve an audit?Compare recorded internal calculations with behavior. Test whether an auditor can detect hidden behavior and distinguish it from an innocent response.
Who controls a shared result?Design rules for joint decryption and result access when several people or organizations use one cloud service.
Why we also advocate peaceful coexistence
The same concern runs through our work: who gets to exercise power over whom? We argue that creating an intelligence does not by itself give its creators a permanent right to rule it.
Read our position on coexistence ↗The connection to our PQC work
Protect data on the way to AI.
Keep it protected during use.
A secure connection protects a document while it travels to a server. A conventional AI service then processes the readable document. Fully Homomorphic Encryption lets a compatible computation run while the document stays encrypted.
Post-Quantum Cryptography strengthens the connection against future quantum attacks. Fully Homomorphic Encryption protects data during computation. That is why these two efforts belong in one security plan.
Protect data in transit.
Use post-quantum key establishment and authentication to protect exchanges and identify the services and people involved.
Keep inputs encrypted.
Use Fully Homomorphic Encryption for supported AI calculations, with the decryption key held by the intended recipient.
Choose who can read results.
Set permissions for decryption and sharing, including decisions that require approval from several participants.
Verify what actually ran.
Check that an answer or audit record came from the agreed model and computation. This adds evidence about execution to the privacy protection.
Why lattice-based encryption connects the two.
Many important post-quantum schemes and constructions of Fully Homomorphic Encryption draw on related hard mathematical problems involving lattices. This lets us bring knowledge of cryptographic assumptions, parameter choices, and key management to both areas.
The protocols still serve different purposes and use their own keys. Post-quantum cryptography also includes approaches such as hash-based signatures.
How this extends our existing security framework
Our Quantum-Enhanced Hybrid Cryptography framework brings together key management, identity, and encrypted communication. The AI research adds encrypted computation and independently checked audit records to that design. The work uses software-based cryptography on classical computers.
In an audit, Fully Homomorphic Encryption protects the contents of the records. Verification addresses whether those records came from the computation being examined.
Research on verifiable Fully Homomorphic Encryption ↗Research & perspectives
Read the arguments
behind our research.
Start with the question that interests you: living peacefully with AI, keeping a conversation private, checking model behavior, or understanding the mathematics.
Peace With AI Cannot Mean Permanent Human Rule
A future AI asks for a decision about its existence to be reviewed. Who writes the rules, and can an appeal actually change the outcome?
Read the essay ↗Private thoughtFully Homomorphic Encryption and the Right to Think
Follow one unpublished idea through an encrypted AI conversation, then see how several organizations could collaborate without exposing their inputs to the cloud provider.
Read the essay ↗Independent scrutinyMove the Audit Outside the Model
A toy model behaves differently during an audit. Follow the test to see what encryption hides, what an independent recorder captures, and how an auditor could check the result.
Read the essay ↗Limits of intelligenceCan Intelligence Outgrow Cryptography?
Use encrypted research notes, repeated weather reports, and a proposed brain mechanism to examine what greater intelligence can—and must still prove it can—overcome.
Read the essay ↗From our PQC library
Explore the foundations
Watch: lattice-based quantum-resistant encryption
About Bay Area Secure
Post-quantum security services.
Research on private AI.
Based in Sunnyvale, California, Bay Area Secure helps organizations assess cryptographic risks and plan the transition to post-quantum algorithms.
Our AI Safety research explores how Fully Homomorphic Encryption could protect people’s questions and documents during cloud AI use, and how independent audit records could help examine model behavior.
Both efforts serve the same purpose: give people and organizations more control over their information as computing systems become more powerful.
Start a conversation
Plan a PQC migration.
Discuss private AI.
For PQC work, tell us which systems and data you need to protect. For research, tell us what you want to keep private or what model behavior you want to examine.
info@BayAreaSecure.com+1 (415) 741-5790Bay Area Secure Inc.440 N Wolfe Rd
Sunnyvale, CA 94085