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Use case 109

AI Safety Evaluation Sandboxes

A deployment concept for AI safety teams, red teams, regulators, laboratories and model providers.

Proposed deployment · Compatibility assessment required
Artificial Intelligence and Data Systems

Why this environment matters

For AI safety evaluation sandboxes, the system tests models for harmful outputs, jailbreaks, tool abuse and unexpected behaviour. The risk extends beyond a conventional endpoint: evaluation prompts or deliberately malicious artefacts can attack the tester's own infrastructure or reach production credentials. A NØNOS deployment concept would treat every software component, data source and device interface as separately authorised rather than assuming that anything running on the host should be broadly trusted.

The security challenge

AI systems connect large datasets, opaque models, external prompts and increasingly powerful tools. A model should not inherit the full authority of the host merely because it was invited to answer a request. In AI safety evaluation sandboxes, the decisive risk is that evaluation prompts or deliberately malicious artefacts can attack the tester's own infrastructure or reach production credentials. Even strong perimeter controls may not help once authorised software, a vendor tool or a valid user session has been compromised. Internal permission boundaries must remain enforceable after initial access.

How the capsule model could help

For this system, NØNOS could run each evaluation in a disposable capsule with mock tools, synthetic secrets and no ambient access to internal systems. The design would combine attested model loading, ephemeral agent sessions, verifiable execution evidence and dataset-scoped capabilities. The intended result would be a set of small trust boundaries instead of one large operating environment where every service inherits broad ambient access.

Separate address spaces and capability checks can limit cross-process reach. They cannot stop harmful use of legitimate permissions, prove AI decisions correct or substitute for domain-specific safety controls.

Deployment requirements

Operating-system isolation cannot prove that a model is accurate, fair or safe. Model evaluation, human governance, data quality, monitoring and domain-specific controls remain necessary.

Current public-beta limitations, hardware support and application availability must be assessed before any pilot. Neither this use case nor an industry source establishes NONOS certification or a current customer deployment.

Who could buy or integrate it?

  • AI research institutes purchasing isolated evaluation compute and tools
  • Model developers funding internal safety-testing infrastructure
  • Independent AI assurance firms integrating reproducible test environments

Industry examples: UK AI Security Institute, Anthropic. Organisations shown illustrate the industry. No NONOS customer, partner or endorsement relationship is implied.

Market opportunity

Market benchmarks and device scenarios.

Published industry benchmark
US$2.8 billion

AI trust, risk and security management

Global · 2025 · annual market estimate

AI governance, explainability, model operations, risk and security products and services; broader than evaluation infrastructure.

Modelled global devices
5K–300K

Candidate OS endpoints

Hypothetical planning range · 2025

Low confidence: planning assumptions. Hardware compatibility, procurement and adoption have not been validated.

Illustrative annual licensing
$750K–$180M

USD / year at full model coverage

Device scenario × assumed US$150–$600 per device / year.

Not a revenue forecast, announced price or measured serviceable market.

Device calculation

Hypothetical global planning range, 2025 scenario: assume 500–5,000 AI labs, testing organizations and regulated model-development teams × 10–60 candidate OS endpoints per site/asset = 5,000–300,000 endpoints. Counting unit: evaluation worker hosts; evaluation runs excluded. Site and asset counts, and devices per site, are planning assumptions. The installed base has not been measured. Coverage is limited to the defined equipped subset; includes all candidate endpoints within that assumed subset. Hardware eligibility, certification, adoption and achievable NØNOS share are unverified; overlaps other cases.

AI trust, risk and security management market report ↗

Market context only; separate from device and site population estimates. Original monetary-market scope and geography are preserved in benchmark. This source does not establish the assumed worldwide site count or endpoint density.

How to interpret the figures

Adjacent or broader commercial market benchmark; not the NØNOS OS market, licensable-device count or revenue forecast.

Modelled candidate endpoints multiplied by an assumed annual USD price per endpoint. Pricing is a planning assumption, not a vendor quote. This illustrates the full scenario range, not revenue or total addressable market. It excludes adoption timing, procurement, certification, support costs, channel economics and achievable market share. Use cases can overlap, so their totals do not represent unique devices.

Research from 2026. Publisher estimates have not been independently audited.

Read the full methodology

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