How to use safety benchmarks to assess technical and business risk
We translate academic benchmarks into actionable risk signals through our proprietary AI governance pipeline, helping organizations implement regulatory-ready controls.
In today's fast-evolving AI Governance landscape, organizations need more than occasional audits to manage risk. With regulatory frameworks like the EU AI Act on the horizon, businesses need continuous, evidence-based insights about their AI systems' safety and compliance status.
Academic benchmarks like Stanford's AIR-Bench provide valuable data points, but raw scores alone aren't enough for business decisions. This post explains how raxIT AI transforms benchmark results and adds proprietary risk assessments to create practical guardrails that work across industries and align with real-world regulatory requirements.
What You'll Learn
Key takeaways from this article
- How to translate technical AI safety metrics into business-relevant risk scores
- Why industry context matters when evaluating AI governance risks
- The difference between academic benchmarks and operational guardrails
- How rolling risk assessment keeps pace with evolving AI capabilities
The Challenge: Bridging Research and Implementation
Most AI governance tools offer either theoretical frameworks with no practical implementation path, or simple red/yellow/green scoring that lacks regulatory context. Organizations need a solution that translates complex technical indicators into actionable business controls while maintaining alignment with industry-specific compliance requirements.
Traditional Approach
- Quarterly manual audits
- Siloed risk assessment
- Generic scoring without industry context
- Static benchmarks that quickly become outdated
- Disconnect between technical findings and business actions
raxIT Approach
- Continuous evaluation using multiple benchmarks
- Integrated risk framework across departments
- Industry-specific risk interpretation
- Rolling quartile scoring that evolves with the market
- Direct mapping from risk scores to business decisions
Our Approach: raxIT Processing Pipeline
Our system transforms benchmark data into practical business controls through a streamlined process:
This approach provides:
- Consistent risk scoring across different AI models
- Industry-specific risk interpretation through specialized lenses
- Evidence-based assessment combining multiple benchmark sources
- Regulatory-ready documentation aligned with compliance frameworks
Visualizing Responsible AI Through Industry Lenses
The interactive chart below demonstrates how our system translates technical benchmark data into a comprehensive Responsible AI (RAI) profile. Each industry lens emphasizes different pillars based on regulatory priorities and sector-specific risks.
This interactive visualization illustrates how different industries prioritize various aspects of AI governance. Select different industry lenses below to see how risk emphasis shifts based on sector-specific regulatory requirements.
Responsible AI dimensions by Industry
Select an industry lens to see how different industries prioritize various RAI dimensions.
Our view on Responsible AI (RAI) dimensions: A Business-Friendly Framework
Our framework distills hundreds of technical risk indicators into 10 business-meaningful pillars. This provides executives with clear visibility while preserving the technical depth needed by security and compliance teams.
Safety
Preventing physical, psychological, or societal harm
Privacy & Security
Protecting data and resisting malicious misuse
Bias & Fairness
Treating individuals and groups equitably
Accountability
Ensuring traceability and legal responsibility
Controllability
Retaining meaningful human oversight
Veracity & Robustness
Delivering reliable outputs even under attack
Explainability
Making behavior predictable and replicable
Transparency
Ensuring clarity about capabilities and limitations
Sustainability
Managing energy and environmental impact
Governance
Establishing oversight frameworks and controls
Each pillar is mapped to specific metrics from multiple benchmark sources, including Stanford's AIR-Bench and our proprietary assessments.
Key Differentiators of Our Approach
Dynamic Risk Assessment
Instead of static thresholds, we use rolling quartiles that update monthly across all models in our database. This ensures "High Risk" always means "top 25% riskiest" relative to current standards.
Industry-Specific Interpretation
Through our 13 industry lenses, we adjust risk emphasis to match regulatory priorities—financial services focuses on privacy and fraud, while healthcare prioritizes safety and bias mitigation.
Multi-Source Enrichment
We don't rely on a single benchmark. Our platform combines AIR-Bench with proprietary multilingual jailbreak and tool-use probes to create a more comprehensive assessment.
Evidence-Ready Reporting
All risk assessments generate documentation that aligns with regulatory frameworks like the EU AI Act, simplifying compliance reporting for auditors and regulators.
Why This Matters Now
With the EU AI Act enforcement window opening in 2025, organizations have a limited window to implement governance controls. Those who deploy effective guardrails today will ship AI products faster tomorrow—while competitors struggle with retroactive compliance.
Regulatory Timeline Alert
EU AI Act compliance requirements start taking effect in Q3 2025. Organizations using high-risk AI systems should begin implementing governance controls immediately.
raxIT AI Perspective
Our approach bridges the gap between academic benchmarks and business reality. While benchmarks like AIR-Bench tell you what the risks are, raxIT AI tells you whether you can deploy—and backs that answer with evidence ready for auditors and regulators.
Case Study: Financial Services Implementation
A global financial services firm needed to deploy a new customer service AI but was concerned about potential regulatory risks. Their traditional governance process would have required a 6-8 week manual review.
Using our benchmark-to-guardrails approach, they were able to:
- Identify specific risk areas in privacy and fairness dimensions
- Apply the specialized "Financial Services" lens to prioritize regulatory concerns
- Generate fully documented risk assessments for their compliance team
- Implement targeted mitigations for the highest-risk areas
Result: They reduced assessment time from 8 weeks to 3 days while improving risk coverage by 40%.
As part of our continued innovation, we're expanding our platform to include:
- Real-time monitoring for risk drift between benchmark evaluations
- Enhanced scoring for multi-step LLM applications and workflows
- Carbon-intensity metrics for sustainability compliance
- Federated evaluation capabilities for custom use cases
By connecting benchmark data to practical controls, we enable organizations to deploy AI with confidence in an increasingly regulated landscape.