AI safety
AI safety that holds up in production, and in front of a risk committee.
Safety talk is cheap; evidence is not. AetherLab operationalizes AI safety as three connected capabilities: adversarial testing that finds real failures, guardrails that contain them on every production check, and documentation institutions can approve.
Safety you can measure beats safety you assert.
Every claim in this workflow is checkable: findings are reproducible, guardrail verdicts carry rationales, and the evidence trail survives an audit.
This is running infrastructure, not a framework PDF.
150,000+
AI checks per day, text and image
17 billion+
tokens screened monthly
Every hour
production traffic served, for the last 90 days
The same infrastructure has been chosen over Amazon Bedrock Guardrails and Hive AI in head-to-head evaluations in high-stakes workflows. Explore the pillars: AdversarialScan, Guardrails, and the Evidence Pack.
AI safety questions, answered plainly.
- What does AetherLab mean by "AI safety"?
- The operational kind: preventing AI systems from producing outputs that harm users, violate policy, or create legal and financial exposure, and being able to prove the prevention works. We red-team systems adversarially, enforce bespoke guardrails in production across text and images, and document both in evidence institutions can act on.
- How is this different from model providers' built-in safety?
- Provider safety layers enforce the provider's general policy. Institutions need their own line held: payment-network rules, underwriting criteria, platform terms, sector regulation. AetherLab enforces your policy, 225+ custom rules per customer today, and tests against your definition of failure, not a generic one.
- Does AI safety here cover images or just text?
- Both, as first-class surfaces. AdversarialScan attacks image generation and image understanding as well as text and multi-turn conversation, and MediaGuard enforces visual policy in production on every check. Much of today's highest-consequence AI risk is visual.
- Is any of this backed by published research?
- The team's published work is in adversarial robustness and model evaluation, including peer-reviewed adversarial-ML research at IEEE ICMLA 2019, before adversarial AI was a commercial category. The attack methodology inside AdversarialScan is proprietary and deliberately unpublished.
Make your AI safety case with evidence.
Securing your own product or evaluating someone else's, the workflow is the same: scan, guard, prove.
Ask about the Evidence Pack
Leave your email and we'll walk you through what an Evidence Pack contains for your use case: severity-scored findings, business-impact mapping, and the approval record.