Secure AI evaluation for regulated industries.
Pallas Exchange is a trusted AI sandbox from SAS Pallas Analytics that allows government agencies and regulated organizations to safely evaluate third-party ML and AI developers and enable secure model development and evaluation - without letting sensitive data leave the system.
Active challenge
Detect prohibited items in CT luggage scans using hidden benchmark datasets in a secure compute environment.
Sandbox policy
No raw export · role-based access · activity logging · reproducible evaluation.
Qualified vendors invited
Top benchmark accuracy
From challenge launch to shortlist
Leaderboard preview
Objective model comparison across accuracy, robustness, and latency.
Container v3.2 · explainability complete
Latency optimized · fairness check passed
Adversarial resilience above threshold
Valuable data exists. Safe external collaboration does not.
Government and regulated organizations have datasets that could dramatically improve AI systems, but they often cannot safely share that data with external vendors. The result is weaker model development, slower procurement, and less competition.
Vendors train on inferior, stale data
Without controlled access to real operational datasets, ML vendors often train on synthetic, incomplete, or lower-value data.
Procurement lacks objective testing
AI vendors are too often selected through proposals and demos instead of measurable performance on hidden benchmark datasets.
Incumbents stay entrenched
When new entrants cannot access secure evaluation environments, agencies remain dependent on large established contractors by default.
Innovation becomes slow and expensive
Without a trusted AI sandbox, pilots take longer, comparisons are inconsistent, and teams spend more to get less meaningful progress.
Bring models to the data
Developers bring models to the data—allowing secure evaluation without exposing sensitive datasets. Pallas Exchange orchestrates secure model testing, benchmarking, and AI challenge workflows, allowing organizations to collaborate with third-party ML developers, compare model performance objectively, and accelerate procurement decisions—while maintaining full governance, auditability, and data protection.
What the platform enables
Why buyers choose it
Pallas Exchange gives organizations a trusted AI sandbox where developers come to the data, models are evaluated in-place, and only approved outputs leave the environment. That makes innovation possible without weakening security boundaries.
- Enable transparent procurement based on measured performance
- Unlock access for smaller, more specialized AI startups
- Reduce dependence on legacy contractors
- Create repeatable competition infrastructure for future procurements
See how internal teams manage active AI challenges, vendor access, and procurement decisions in real time.
This sample dashboard shows how an agency or regulated organization can track participation, compare top-performing models, manage secure vendor access credentials, and turn evaluation outputs into procurement-ready decisions.
Participation rate by vendor cohort
Track which approved vendors are actively participating so teams can monitor challenge health and identify where support or extensions may be needed.
Top-performing model metrics
Easy-to-read visualizations help technical, procurement, and oversight teams compare top models by accuracy and operating profile at a glance.
Leaderboard
Compare top submissions across accuracy, robustness, and latency in one audit-friendly leaderboard built for objective vendor evaluation.
| Rank | Vendor | Accuracy | Robustness | Latency | Status |
|---|---|---|---|---|---|
| 1 | Sentinel Vision Labs | 97.3% | 94.8% | 132 ms | Approved |
| 2 | Northbridge AI | 96.8% | 93.9% | 118 ms | Approved |
| 3 | Helios Secure ML | 95.9% | 96.1% | 146 ms | Approved |
| 4 | Aegis Compute | 94.6% | 92.3% | 121 ms | Review |
Run secure ML vendor competitions.
Pallas Exchange provides a controlled AI evaluation platform for regulated organizations to assess third-party machine learning models against sensitive datasets without moving the data outside their secure environment. The platform orchestrates governed access, reproducible scoring, and benchmarking workflows that generate procurement-ready performance evidence.
Govern and prepare evaluation datasets
Organizations register datasets inside their secure compute environment with version control, privacy safeguards, access governance, and full audit logging to ensure sensitive data remains protected while enabling controlled model evaluation.
Define an AI evaluation challenge
Organizations publish a structured evaluation task, including the model objective, scoring metrics, approved submission formats, and participation timeline for vendors.
Execute secure model evaluations
Developers submit models that run inside the governed evaluation environment. Models are tested against hidden datasets without exposing or exporting the underlying data.
Benchmark vendors and support procurement
Automated evaluation generates performance leaderboards, benchmarking dashboards, validation documentation, and procurement-ready reports to support transparent vendor selection.
Everything needed to operate a trusted AI sandbox.
Pallas Exchange supports secure data access, model submission, evaluation, and repeatable vendor competitions in one navigable platform.
Secure compute environment
Give vendors controlled access to sensitive datasets without permitting downloads, copying, or unmanaged external handling.
Automated evaluation engine
Run standardized pipelines for accuracy tests, bias checks, robustness checks, adversarial testing, explainability metrics, and compute performance.
Real-time leaderboards
Rank vendors objectively on hidden test datasets and show performance changes as improved models are submitted.
Procurement-ready reports
Produce evaluation reports, benchmarking summaries, and model validation documentation that support faster procurement decisions.
Continuous model improvement
Allow organizations to re-run evaluation automatically as developers upload updated model versions and improved architectures.
Vendor competition infrastructure
Launch controlled competitions that help agencies discover high-performing vendors and broaden participation beyond incumbents.
Start with vendor competitions. Expand into secure collaboration.
Manage data access and sharing, control experimentation, and host internal and external model testing on an ongoing basis.
Phase 1: Vendor competition platform
Government agencies launch secure model competitions to identify the best ML vendors through transparent leaderboards, performance benchmarking, and procurement-ready evaluation artifacts.
Aviation security example
Run a challenge such as detecting prohibited items in CT luggage scans, where developers submit models and receive scores against hidden benchmark datasets.
Phase 2: Secure data collaboration
Extend the platform to manage sensitive datasets, enable controlled experimentation, and host internal and external model testing on an ongoing basis.
Other regulated markets
Apply the same model to healthcare, critical infrastructure, defense, and other environments where sensitive data must stay protected while innovation continues.
Sensitive data never leaves the system.
Pallas Exchange is designed so developers access the environment, not the raw files. Only approved outputs are exported, preserving control while still enabling outside innovation.
Access governance and protected data handling
Use role-based permissions, dataset version control, and complete audit trails to keep data controlled while letting approved vendors work in a governed sandbox.
Privacy-safe technical methods
Support privacy-preserving collaboration using secure enclaves, differential privacy, federated learning patterns, synthetic datasets, and controlled output review.
A five-layer platform for evaluation, competition, and collaboration.
The architecture is designed to support both the near-term competition product and the longer-term secure collaboration roadmap.
Platform layers
Strategic outcome
Pallas Exchange turns restricted datasets into a repeatable innovation and procurement system. Agencies can safely invite third-party ML developers into controlled environments, compare performance objectively, and build a more competitive AI ecosystem over time.
- Create transparent vendor evaluation processes
- Support real-time model updates and continuous improvement
- Shorten vendor selection cycles dramatically
- Lay the foundation for a future AI innovation marketplace
Open innovation. Protected data. Trusted AI evaluation.
Launch a secure challenge through a trusted sandbox, evaluate vendors on real performance, and build a repeatable path from restricted data to faster, more objective procurement.