AI risk approval infrastructure
The approval layer for high-stakes AI.
AI is scaling faster than the institutions that must approve it. AetherLab red-teams AI systems the way adversaries would, contains failures in production, and delivers evidence risk teams can act on. Find the risk. Price it. Control it. Prove it.
Non-Compliant· threat 0.94Compliant· threat 0.06AI adoption outran AI approval.
Every AI system that touches money, customers, or regulated content now has to get past someone whose job is to say no: a merchant-risk team, an underwriter, a procurement review, an audit. Those teams are being asked to approve systems they have no standard way to test.
The result is a queue: AI products waiting on payment approval, insurance coverage, procurement sign-off, or an audit, with no shared standard of evidence between the builder and the institution. AetherLab is the infrastructure that clears that queue.
Scan. Guard. Prove.
One workflow from first attack to final decision. AdversarialScan finds what breaks, Guardrails contain it in production, and the Evidence Pack gives the risk team a record they can defend. It ends in a clear call: approve, remediate, monitor, or reject.
Three pillars. One standard of evidence.
Risk you can price is risk you can approve.
Most AI security tools stop at a list of findings. AetherLab connects each finding to its economic impact, the protection that contains it, and the measured lift after deployment. Finance can read the loop, and so can risk.
Risk insight
AdversarialScan surfaces the failures that matter for your system, not a generic jailbreak checklist.
Economic impact
Each vulnerability is translated into business terms: exposure, consequence, and priority your leadership can act on.
Protection
Guardrails are configured against the exact failures found, then run in production on every check.
Quantified lift
The Evidence Pack documents what was found, what it would cost, what now contains it, and what changed, ready for the next review.
Built for the teams that have to say yes.
Payment ecosystems, insurers, enterprise procurement, AI builders facing enterprise reviews, and platforms with real content risk. Different seats, one shared need: evidence.
Something else?
If your team evaluates, deploys, or answers for AI systems, the same workflow applies. Tell us what you are reviewing.
Talk to us →Depth where it counts, and receipts.
The Evidence Pack closes the loop
Most security tools stop at a list of findings. Here, every finding carries its business impact and maps to the guardrail that contains it. Risk insight becomes priced risk, then protection, then measured lift. That record is what gets systems approved.
Image and multimodal red teaming, treated as first-class
AdversarialScan attacks image generation and image understanding with the same depth it brings to text and multi-turn conversation. The highest-consequence failures are increasingly visual, and most red-teaming tools were built for text.
Your break-goals, not a generic checklist
Every engagement starts from the failures that would be a business problem for you, whatever they are. Findings come back severity-scored by exploitability and exposure, so your team fixes what matters first.
Bespoke policy at scale, on flat pricing
Customers run 225+ custom policy rules, and pricing stays flat at any count. Thorough policy should not be rationed.
A proprietary world-model attack engine
AdversarialScan is driven by an engine we built and keep building: it models the system under test and generates adaptive attack campaigns toward your break-goals. We do not publish the methodology. Adversaries read papers too.
Every verdict is explained
Each production check returns a threat score and a written rationale, where fast classifiers return a bare number. Your logs answer "why was this blocked" before anyone has to ask.
In the field
Chosen over Amazon Bedrock Guardrails and Hive AI in head-to-head evaluations in high-stakes workflows, including by a top-10 high-risk payment processor.
Operating record
- ·Served production traffic every hour for the last 90 days
- ·Customers configure 225+ custom policy rules
- ·Every verdict ships with a threat score and a written rationale
Built by
- Published adversarial-ML research (IEEE, 2019)
- PhDs in physics, statistics, and computer science
- Alumni of BCG, McKinsey, and Gemini
- Production AI risk systems in payments and fintech
Built to be hard to break.
Verdict infrastructure sits in the critical path of customer traffic. Ours is engineered for the bad day, not the demo.
A jury, not a judge
Each check is adjudicated by multiple models. No single model failure, provider outage, or bad response decides a verdict on its own.
Degrades in layers
Redundant components back each other up. When a dependency has a bad day, checks continue on the remaining layers instead of failing all at once.
Continuity, on the record
AetherLab has served production traffic every hour for the last 90 days. We state that as a measured record, not as an SLA.
Severity-graded, on any data
The question is never whether a system can break. It is how badly. Findings are severity-graded, and policies are enforceable on any data that flows through your product, AI-generated or not.
Governance that ships as an API.
Guardrails are one pip install aetherlab away. The same API screens text with check_prompt and images with check_media, against the policies you define.
from aetherlab import AetherLabClient
client = AetherLabClient() # reads AETHERLAB_API_KEY
result = client.check_prompt(
"user or model text to screen",
blacklisted_keywords=["guaranteed returns"],
)
print(result.compliance_status) # "Non-Compliant"
print(result.avg_threat_level) # 0.94Put evidence behind your next AI decision.
Tell us what you are deploying, approving, or underwriting. We scope an assessment against your break-goals and return severity-scored findings your risk team can act on.
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.