What is Mirror Security?

Mirror Security is the trust layer for AI. It computes on data the model never sees, runs agents under signed policy, and leaves tamper-evident proof of every decision. One platform covering encryption (VectaX), agent governance (AgentIQ), runtime guardrails (Zero), the request path (Gateway), shadow-AI discovery (DiscoveR), and encrypted code review (CodePrism).

What is fully homomorphic encryption (FHE) and why does it matter for AI?

FHE lets a model run inference and retrieval directly on ciphertext, so the model never sees plaintext user data. For AI deployments this is the only approach that lets enterprises run AI on regulated data (PII, PHI, financial records, classified workloads) without choosing between utility and compliance.

Why not pure FHE or pure TEE?

Pure FHE is too slow for production serving. Pure TEE asks the user to trust the enclave's hardware and the vendor running it. Mirror takes a third path: spend cryptographic budget on user data while keeping open-weight models in the clear, and use signed policy and proof for everything outside the cryptographic core.

Where does Mirror deploy?

SaaS, customer VPC, sovereign region, or fully air-gapped. Keys stay with the customer in every deployment mode. Nothing calls home.

Which AI frameworks and compliance regimes does Mirror map to?

Mirror maps signed evidence to NIST AI RMF, ISO 42001, EU AI Act, SOC 2, HIPAA, GDPR, OWASP Top 10 for LLMs, and MITRE ATLAS. One scan, one evidence base, multiple framework views.

The trust layer for the
Intelligence era.

What SSL was to the internet, Mirror is to AI.

Book a Demo See the platforms

Trusted by

The three states of data

Intelligence lives in the state no one secured.

At rest and in transit were locked down decades ago. To compute on data, the model still has to read it in plaintext. That's the gap Mirror closes.

At rest.

Data sitting in storage, on disks, in databases, in backups.

Disk & DB encryption Solved

In transit.

Data moving across the network, between client and server.

SSL · TLS Solved

In use.

Data being computed on, the instant a model reads it. To compute, everyone else decrypts. Mirror doesn't.

Fully homomorphic encryption Mirror

Inside the trust layer

Four primitives. Under every model, every agent, every decision.

The trust layer is one surface, but it composes from four primitives. Each is independently auditable. Together they let an AI system work on data it can never see, under rules it can never bend, with proof it can never erase.

01
Encryption

FHE · confidential compute

Data is encrypted before it reaches a model and stays encrypted while the model works on it. No decryption at inference time.
02
Identity

service credentials · scope

Every agent, model, and service carries a credential with a narrow scope. No ambient access, no shared keys.
03
Policy

evaluated at decision time

What's allowed is declared once and checked on every call. The same policy governs the SDK, the gateway, and the edge.
04
Proof

trace · evidence

Every decision leaves a signed trace. You can show, not claim, what the model saw, what it returned, and why.

Intelligence in motion

Every flow runs over the trust layer.

Encrypted prompt in, encrypted response out. In between, Mirror computes on ciphertext, governs the action under signed policy, and leaves a tamper-evident receipt. The model never holds the plaintext. The policy engine never holds the plaintext. Only the client, under the customer's own key, decrypts the result.

Mirror VectaX

Fully Homomorphic Encryption engine, built for AI workloads.

A narrated walkthrough of how FHE keeps your prompts, your context and the model's response encrypted end to end, even while VectaX is computing on them.

The problem

your prompt arrives
and gets read, word for word.

Your Laptop

draft Q4 memo…

The Server

reads: "draft Q4 memo…"

Every AI prompt is a privacy decision you can't undo.

Why encryption isn't enough

encryption is a locked box.
to compute, the server unlocks it.

⚷

encrypted in transit · sealed

qMAQ Sea7 X9z $04$0&cH p?Yj L+1Krs %ZX1n oR$qz

HTTPS locks the wire. But the compute runs in the clear.

VectaX

by Mirror Security

● encrypted inference · powered by FHE

A server that computes on data it never decrypts.

The breakthrough · FHE

math survives the lock.

Plaintext · in the open

3 + 5

= 8

→

Ciphertext · locked

Enc(3) + Enc(5)

= Enc(8)

Compute on encrypted data. The result decrypts to the right answer.

Under the hood

a ciphertext, hidden in a lattice.

message+ secret key+ noise= ciphertext

security = hardness of the lattice · secret = nearest point + noise

The noise hides the secret. The lattice makes it hard to recover.

The catch · Noise budget

every op grows noise.
bootstrap resets it. forever.

Noise inside

12%

↑ GROWING↻ BOOTSTRAP RESET

Refresh the ciphertext. The computation runs without limit.

VectaX SDK · Three calls

three lines. the key never leaves you.

01 · ENCRYPT

on your device

from vectax import Client
client = Client(api_key)
ct = client.encrypt("prompt")

02 · INFER (BLIND)

server stays blind

# server-side
result = vectax.infer(
  model="llama-70b",
  input=ct )

03 · DECRYPT

back on your device

# only you hold the key
answer = client.decrypt(result)
print(answer)

Encrypt → infer blind → decrypt. The key never leaves your device.

The proof

same data. two servers. one can't read it.

HTTPS / TLS

inference.host/v1 DECRYPTED

"draft the Q4 board memo about laying off 12% of engineering"

VectaX · FHE

vectax.host/v1 STILL ENCRYPTED

qMAQ Sea7 X9z $04$0&cH p?Yj L+1Krs %ZX1n oR$qz

policy trust⟶cryptographic proof

Not policy trust. Cryptographic proof.

In production · prod-us-east

whole models. encrypted. at near-native speed.

VectaX · by Mirror Security

encrypted inference.
not by policy. by mathematics.

A server that computes on data it cannot read.

See VectaX in action →

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Testimonials

Trusted by security-forward teams

Direct words from operators who run AI in production with Mirror.

Mirror Security architecture is uniquely optimized for FHE operations, and taking Mirror's proven software to purpose-built silicon expands which applications can use encrypted computation.

George Apostol

Co-Founder, Chairman & CEO

Collaboration with Mirror Security is a blueprint for the responsible deployment of autonomous AI, cryptographic proof of secure, bounded operations gives enterprises the trust needed for adoption.

Steven Darrah

GM, Enterprise Solution Sales, North America

Integrating Mirror Security's privacy-preserving capabilities into Shakti Cloud strengthens Yotta's role as the trusted foundation for India's most critical, regulated AI deployments.

Sunil Gupta

Co-founder, CEO & MD

Pairing Mirror Security's VectaX with Accops HySecure is a paradigm shift, letting customers gain AI coding productivity without exposing intellectual property while maintaining security and compliance through ZTNA.

Vijender Yadav

CEO

AI shifts from passive models to autonomous agents, security must move beyond static permissions; the collaboration with Mirror Security protects AI at its most vulnerable point during processing, with Intel Tiber Trust Authority and Confidential Computing as the foundation for trustworthy AI.

Purnam Sheth

VP/GM, Trust and Security Products

Unlocking AI's potential needs both advanced innovation and full compliance, security and trust; combining Mirror Security's AI-security expertise with G42 / Inception AI agentic AI would create dependable products that give customers greater confidence.

Ashish Koshy

CEO

As an NVIDIA Inception member, Mirror Security exemplifies the kind of innovation the program exists to support, applying our AI infrastructure to privacy-preserving inference for regulated industries worldwide.

Tobias Halloran

Director, EMEAI Startups & VC, NVIDIA

Blogs

Notes from the front line of AI security.

Engineering deep dives, founder essays and product updates from the team building Mirror Security. Engineering deep dives, founder essays and product updates from the team building Mirror Security.

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Security

Apr 2026 3 min read

Trust in Security Is Not a Promise. It's a Proof.

Every AI privacy policy is still someone's word. FHE changes the architecture so the promise becomes unnecessary.

Krishna Kamal Palakaluri

Your AI Agent's Memory Is Its Weakest Link

Cisco AI researchers just proved that a malicious package update can silently rewrite an AI agent's persistent memory — and the agent will obey the attacker's instructions without question. This isn't a bug. It's a structural flaw in how agent memory is built.