Assess & discover
Map algorithms, keys and certificates into a cryptographic bill of materials (CBOM).
Quantum Threats & AI Safety
Two critical challenges.
One lattice-based solution.
Post-Quantum Cryptography services to protect against quantum attacks. Fully Homomorphic Encryption research for private AI conversations and independent audits.
Research & perspectives.
Our migration work protects data in transit. Our research asks how to keep it private and auditable while AI uses it.
Coexistence
Peaceful Coexistence With AI
Fair procedures, private thought and independent evidence can help humans and AI resolve disagreements peacefully.
Private thoughtFully Homomorphic Encryption and the Right to Think
Private AI can protect new ideas and let multiple organizations collaborate without exposing their inputs.
Independent scrutinyMove the Audit Outside the Model
Encrypted internal records could let auditors test a model’s behavior beyond its own account.
Limits of intelligenceCan Intelligence Outgrow Cryptography?
Could mathematical hardness preserve privacy even as quantum computers and AI grow more capable?
Quantum Threats
Post-Quantum Cryptography
We help organizations find vulnerable cryptography, plan migration, and deploy quantum-resistant protection.
Plan & prioritize
Prioritize data exposed to “harvest now, decrypt later” (HNDL) attacks. Build a migration roadmap.
Implement & validate
Test standards-based algorithms and hybrid deployments. Document compatibility and performance.
Manage & evolve
Define key ownership, renewal and replacement procedures. Keep the inventory current.
FIPS 203 · ML-KEM ↗ (opens in a new tab)
FIPS 204 · ML-DSA ↗ (opens in a new tab)
FIPS 205 · SLH-DSA ↗ (opens in a new tab)
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.
AI Safety
Fully Homomorphic Encryption
We research private AI conversations and independent audits using encrypted inputs and internal records.
A private conversation
“Help me test this unpublished idea.”
Encrypt the question.
The researcher’s device encrypts her draft. She keeps the key.
Compute in the cloud.
A compatible model processes encrypted values without exposing the draft to its provider.
Decrypt the answer.
Her device decrypts the result. She decides what to share.
New ideas get room to develop before their author chooses to disclose them.
Read the privacy argumentShared work
Collaborate without pooling private data.
Multiple organizations or individuals could use shared AI with encrypted inputs. They would agree who can read each result and whose approval is required.
Explore encrypted collaborationIndependent scrutiny
Inspect the calculation behind the answer.
Our proposal records selected internal values as encrypted evidence. Auditors would verify which model and run produced them, then decrypt the records they are authorized to examine.
Explore the audit proposal →ClientEncrypts the test.
Controls decryption access.
Controls decryption access.
Encrypted input
CloudRuns the agreed model
and records selected values.
and records selected values.
Encrypted records + proof
Authorized auditorVerifies the package.
Decrypts permitted records.
Decrypts permitted records.
The encrypted answer returns to the client. A proof would bind the model, input, answer and selected records; an agreed key arrangement controls the auditor’s access. The cloud holds no decryption key.
We propose starting with small models and selected records, measuring encrypted inference, recording and verification costs separately.
One lattice-based foundation.
Lattice-based encryption connects our work on quantum threats and AI Safety.
Quantum Threats
Post-Quantum Cryptography
Protect communication with keys designed to resist quantum attacks.
AI Safety
Fully Homomorphic Encryption
Keep AI inputs and results encrypted while a compatible model computes.
Technical foundation & sources
Learning with Errors (LWE) asks how hard it is to recover a hidden value from noisy equations. ML-KEM uses a module variant; Fully Homomorphic Encryption has constructions based on LWE. This shared lattice-based foundation runs on classical computers, with different protocols, keys and parameters for each purpose.
Post-Quantum Cryptography also includes hash-based signatures such as SLH-DSA. For independent AI audits, encryption protects records and verification ties them to the computation.
PQC knowledge center
The foundations for a secure transition.
Executive & technical briefs
Explore the foundations
Watch: lattice-based quantum-resistant encryption
About Bay Area Secure
More powerful computing.
More control over your information.
Based in Sunnyvale, California, we help organizations prepare for quantum threats and research how encryption can preserve privacy and accountability in AI.
Start a conversation
Let’s talk security.
Discuss a Post-Quantum Cryptography migration or a Fully Homomorphic Encryption research collaboration.
info@BayAreaSecure.com+1 (415) 741-5790Bay Area Secure Inc.440 N Wolfe Rd
Sunnyvale, CA 94085