Secure Enclaves for Agentic AI in Quantum Research: Architecture and Demo

Hook — the pain you're feeling, solved

Quantum labs in enterprise settings face a stubborn dilemma in 2026: teams want the productivity gains of agentic AI (autonomous agents that run experiments, triage job failures, and synthesize results) but cannot risk exposing credentials, raw experiment data, or proprietary calibration parameters to third-party models or user desktops. Regulated labs — healthcare, finance, defence and government partners — add compliance, audit and data-minimisation requirements that break simple integrations with agents. This article proposes an architecture that places agentic AIs inside secure enclaves so they can act on experiment data without leaking secrets or raw inputs, and it walks through a reproducible demo you can run or adapt in a private lab.

Why this matters in 2026

Through late 2025 and into 2026, cloud and hardware vendors have expanded confidential computing features and tooling — Nitro Enclaves, AMD SEV, Intel TDX and Azure/Google confidential VMs now offer more mature attestation and key provisioning flows. At the same time, the rise of tabular foundation models and agentic augmentations (Forbes coverage in Jan 2026 highlighted these shifts) means agents are increasingly useful for analysing structured experiment outputs and making autonomous decisions. The intersection of agentic AI plus more accessible confidential computing enables a new pattern: run agents where the data sits, inside a Trusted Execution Environment (TEE), and share only minimal, provable artifacts with the outside world.

High-level proposition

Run agentic AI processes inside secure enclaves that have:

• Ephemeral secrets provisioned via hardware-backed attestation (no long-lived credentials inside the enclave)
• Strict data minimisation: agents receive tokenised, aggregated or differentially private summaries, never raw telemetry or unredacted audit trails
• Fine-grained access control and policy enforcement at the host boundary (RBAC + OPA-style policies + audit logs)
• Cryptographic attestation results for auditing and reproducibility

Threat model and regulatory requirements

Define scope clearly before you start:

• Adversary: insider or cloud operator who can inspect host OS but not TEE internals; remote attacker who can tilt network connections.
• Assets to protect: quantum experiment raw measurement data, hardware control credentials, API keys for cloud quantum services, and calibration parameters.
• Requirements: non-export of raw data outside on-prem/networks, auditable attestation, ability to revoke agents and keys, and demonstrable data-minimisation for compliance (GDPR/UK DPA/HIPAA where applicable).

Proposed architecture — components and flows

At a glance, the architecture has these components:

1. Host & Enclave: physical or cloud instance that runs a TEE (Nitro Enclaves, AMD SEV, Intel TDX or Enarx WASM enclaves).
2. Agent Container / Enclave Image: contains the agent runtime (small LLM or tabular foundation model), orchestration loop, and logic suited to the lab.
3. Key Broker Service (KBS) + KMS: attestation-aware service that provisions ephemeral secrets to an enclave after verifying attestation docs.
4. Data Sanitiser / Aggregator: service that reads raw experiment outputs and produces minimised payloads — aggregates, hashed IDs, DP-noised counts, or tokenised tables.
5. Policy Engine: OPA-style policy that enforces what the agent can ask for, actions it can request (e.g., run job, request calibration), and logging rules.
6. Audit & Telemetry: immutable logging (append-only) and signed attestation results stored off-enclave for audits and reproducibility.

Data flow (short)

1. Raw experiment data lands in a protected, on-prem store (or private cloud). A sanitiser extracts only permitted fields and computes aggregates or tokenised rows.
2. The agent enclave requests ephemeral keys from KBS. KBS validates enclave attestation and issues short-lived credentials or an encrypted envelope that the enclave can decrypt.
3. The agent inside the enclave consumes the minimised payload, performs computations (e.g., identify drift in qubit calibration), and optionally requests a privileged operation via a signed request that the host operator must approve according to policy.
4. All actions and returned artifacts are logged, attested, and anatomised for audits.

Data minimisation patterns for quantum labs

Minimisation is the central safety knob. Use these patterns when sanitising experiment outputs for agents:

• Aggregates and histograms: provide summary stats (counts, medians, variance) instead of per-shot counts unless per-shot is strictly necessary.
• Feature vectors: convert results to derived features (calibration drift score, readout fidelity metric) that strip identifying metadata.
• Tokenisation and hashing: replace lab identifiers with salted hashes held outside the enclave so linkage is controlled.
• Differential privacy: add calibrated noise to counts or values when statistics are shared across teams or external partners.
• Tabular foundation model friendly format: structure the sanitised output as a narrow, well-typed table — this improves agent accuracy when using tabular models without exposing raw traces.

Demo: run an agentic AI inside a Nitro Enclave (pattern you can adapt)

This demo sketches a reproducible pattern you can adapt to other TEEs. It uses:

• A host instance with Nitro Enclaves (on-prem or AWS Nitro)
• A Key Broker Service that validates attestation and requests short-lived KMS grants
• A sanitiser that produces minimised tabular payloads from quantum experiment results
• A small tabular model inside the enclave (a quantised HF model or a compact MLP) acting as the agent brain

Prereqs

• Linux host with Nitro Enclaves or equivalent TEE
• nitro-cli (or your vendor SDK)
• Python 3.10+, pip, and lightweight ML runtime (onnxruntime or bitsandbytes for quantised models)
• A key management system (AWS KMS, Azure Key Vault) and a KBS pattern implemented as a service

Sanitiser example (Python pseudocode)

<code># sanitiser.py - runs outside enclave, reads raw results, emits minimised CSV
import pandas as pd
import hashlib

RAW_PATH = '/data/quantum/raw_results.csv'
OUT_PATH = '/data/quantum/minimised.csv'
SALT = b'some-lab-salt'  # rotate and protect

df = pd.read_csv(RAW_PATH)
# compute derived features
features = pd.DataFrame()
features['job_hash'] = df['job_id'].apply(lambda j: hashlib.sha256(SALT + str(j).encode()).hexdigest())
features['mean_fidelity'] = df[['shot_1','shot_2','shot_3']].mean(axis=1)
features['readout_error'] = df['readout_error']
# aggregate per-job
agg = features.groupby('job_hash').agg({'mean_fidelity':'mean','readout_error':'median'}).reset_index()
# write a tight tabular payload
agg.to_csv(OUT_PATH, index=False)
print('Minimised payload written to', OUT_PATH)
</code>

Key Broker Service (KBS) pattern

KBS runs as a hardened service that accepts an enclave attestation document and returns a short-lived encrypted envelope containing KMS grants. The flow is:

1. Enclave presents attestation doc (signed by TEE hardware root)
2. KBS validates attestation (checks PCRs, image digest, and expected agent image fingerprint)
3. KBS calls KMS to create a grant tied to the attestation and returns an encrypted envelope the enclave can decrypt

Enclave-side secret retrieval (illustrative)

<code># enclave_agent.py - runs inside enclave
import vsock  # pseudo-interface for enclave-host socket
import subprocess

# request encrypted envelope via vsock to host KBS helper
sock = vsock.connect(port=5001)
sock.send(b'REQUEST_SECRETS')
enc_envelope = sock.recv()
# decrypt using enclave-native key (provided by attestation flow)
# decrypt_envelope() is a placeholder for the enclave's decryption path
secrets = decrypt_envelope(enc_envelope)
API_KEY = secrets['quantum_api_key']

# load minimised data (mounted read-only into the enclave)
import pandas as pd
payload = pd.read_csv('/mnt/minimised.csv')
# run a small tabular model to make recommendations
from model import TabularAgent
agent = TabularAgent(model_path='/opt/agent/model.onnx')
recommendations = agent.act(payload)
# send a signed decision back to host for operator approval
signed_req = sign_request(recommendations)
vsock.send(signed_req)
</code>

Notes: the host-side helper that speaks to KMS never exposes secrets into the host OS; it only provides encrypted envelopes after validating attestation. The enclave holds the only runtime key that can decrypt the envelope.

Integration with quantum SDKs and workflows

Many labs use SDKs like Qiskit, PennyLane, Cirq or Q# to submit jobs to hardware or simulators. Integration patterns:

• Keep job submission credentials outside the enclave. The enclave can prepare a signed, minimal job spec and submit it to a queue; a host-side gate (policy engine) reviews the signed spec and proxies to the cloud or hardware provider.
• For on-prem hardware control, provide the enclave with a narrow RPC to request actions (e.g., start-job) and only accept signed responses from the enclave after attestation checks.
• Use the sanitiser to convert raw measurement counts into tabular features the agent understands. This is especially important for tabular foundation models that prefer fixed schemas.

Policy, RBAC and audit (operational checklist)

1. Define actions the agent can request: run job, recommend calibration param, or change schedule. Map each to an approval flow.
2. Use an OPA/Conftest policy server to validate signed enclave requests before action.
3. Ensure attestation documents and key issuance events are immutably logged in an append-only store (object store with WORM or ledger).
4. Rotate salts, ephemeral keys and enclave images regularly and replay attestation checks during image deploy.

Performance and practical trade-offs

TEEs restrict resource usage. Running a full 70B LLM inside many enclaves is impractical today; instead, use:

• Small, quantised tabular models (mm-sized or ONNX MLPs)
• Split compute: agent brain in enclave, heavy non-sensitive compute outsourced to non-sensitive hosts with proofs of provenance
• Hybrid approach: enclave performs decisioning and signs requests; non-sensitive analytics tasks run outside and return results only after validation.

Advanced strategies (2026-ready)

For larger-scale or cross-institution experiments:

• Enclave federation & MPC: combine TEEs with secure Multi-Party Computation for federated model training across institutions without sharing raw datasets.
• Confidential containers: use Kata/Enarx to run containerized enclaves for easier CI/CD and reproducible agent images.
• Verifiable compute: attach reproducible attestation results and signed transcripts to model outputs so auditors can verify what the agent saw and decided.
• Tabular foundation model fine-tuning: host the fine-tuning pipeline in an enclave to protect proprietary calibration data; expose only the final distilled weights as allowed by policy.

Compliance, auditability and evidence

Document the following artifacts for a compliance audit:

• Enclave image digest and build recipe
• Attestation documents for each run
• Key issuance events (who approved the KBS grant)
• Sanitiser transformation rules and configuration (how raw -> minimised mapping is done)
• Signed decision logs from the agent and the host gate

Case study: lab scenario (concise)

Imagine a UK health-research quantum lab running calibration sweeps nightly. An agent within an enclave analyses nightly telemetry (minimised), detects a slow drift in readout fidelity, and proposes a recalibration schedule. The host gate requires a human operator to approve agent-signed requests for any hardware changes. Auditors later verify the signed attestation and the sanitiser's transformation that ensured no patient IDs left the enclave. This pattern satisfies data protection obligations while allowing autonomous triage.

Operational checklist before production

1. Threat model review and data-classification exercise
2. Build reproducible enclave images with SBOMs
3. Implement KBS with strict attestation validation and short TTLs
4. Automate sanitiser tests with property-based checks to ensure no PHI leaks
5. Define operator approval SLAs and escalation paths for agent-signed actions
6. Run red-team exercises to validate the host-to-enclave and enclave-to-KBS flows

Limitations and realistic expectations

Secure enclaves tighten the attack surface but are not a silver bullet. Hardware-level vulnerabilities, side-channels, and supply-chain risks remain. Treat TEEs as one layer in a defence-in-depth strategy: combine code audits, network segmentation, hardware lifecycle management, and policy governance.

"Confidential computing is a powerful enabler, but the real safety comes from combining strict data minimisation with verifiable attestation and operational controls." — Practical guidance for regulated quantum labs, 2026

Actionable takeaways

• Start small: prototype with a compact tabular model inside an enclave and a sanitiser that emits only aggregates.
• Use an attestation-aware KBS to avoid embedding long-lived credentials in the enclave.
• Build policy gates that require operator approval for high-risk actions; log everything with signed artifacts for auditability.
• Consider federated approaches (MPC + TEEs) for cross-institution projects to protect proprietary datasets.

Next steps (hands-on)

1) Implement the sanitiser and run the minimised payload through a constrained tabular model in a local enclave. 2) Add a KBS stub that validates a synthetic attestation and issues a short-lived secret. 3) Instrument signed logging and run a compliance checklist against your lab policies.

Conclusion & call to action

In 2026, placing agentic AIs in secure enclaves is the pragmatic path for regulated quantum labs that need autonomous capabilities without compromising secrets or raw data. The architecture described here — enclave runtimes, attestation-backed KBS, strict data minimisation, policy gates, and auditable logging — is production-ready in pattern if not turnkey. If you manage a quantum lab or build tooling for enterprise quantum, start a proof-of-concept that implements the sanitiser + enclave + KBS loop for a single, well-scoped use case (e.g., nightly calibration triage).

Want a tailored blueprint for your lab? Contact our team at smartqubit.uk for a hands-on workshop that maps this architecture to your stack (Qiskit/PennyLane/Cirq) and compliance needs. We'll help you design the KBS, sanitiser rules, and a test harness so you can safely bring agentic automation into production.

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