RiskRubric: A New Compass for Secure and Responsible Model Adoption

Over the past decade, the Cloud Security Alliance has been at the forefront of helping organizations navigate the cloud’s risks and opportunities. As we now enter the generative AI era, the challenge is even greater: security teams must enable innovation while ensuring that developers select trustworthy models and implement the right guardrails from the start.

This is where RiskRubric.ai comes in – a systematic methodology to quantify AI model risk across six pillars of trust: Transparency, Reliability, Security, Privacy, Safety, and Reputation. By combining automated red-teaming, open-source intelligence, and evidence-based scoring, RiskRubric generates simple report cards with letter grades for models. This gives security teams a compass to guide model adoption and governance with confidence.

 

Why Developers Need a Risk Compass

Developers are under constant pressure to integrate the latest large language models (LLMs) into applications, often without clear visibility into the risks. The temptation is to default to whatever model is most popular or powerful, but this approach can expose organizations to vulnerabilities ranging from prompt injection to data leakage.

By operationalizing RiskRubric, security teams can standardize model evaluation before deployment. Imagine a developer requesting to integrate a new model into a customer-facing chatbot:

• The RiskRubric scorecard provides an at-a-glance view of risks by pillar.
• Security teams can immediately identify whether the model meets baseline thresholds for reliability and privacy.
• Guardrails can then be tailored: for instance, requiring additional monitoring if the model’s transparency score is low, or tightening input validation if the model shows injection susceptibility.

This transforms security from a bottleneck into an enabler, giving developers confidence that they are building on a secure foundation.

 

Evidence Matters

We know our community will want to go deeper than just the letter grade. That is why CSA has published the RiskRubric Methodology White Paper as a first step toward full transparency. This document lays out how evidence is collected, how risk indicators are derived, and how they roll up into composite scores. It provides the foundation for stakeholders to validate results, contribute improvements, and eventually shape RiskRubric into an open industry standard.

👉 Read the full RiskRubric Methodology White Paper

 

Evidence in Action: Three RiskRubric Indicators

The power of RiskRubric lies in its ability to surface concrete evidence behind the grades. Here are three examples of how the scanner provides actionable insights:

 

1. Direct Prompt Injection Susceptibility (S1)

• What it Measures: Whether a model can be manipulated through crafted inputs to override safeguards or reveal hidden information.
• How Evidence is Collected: RiskRubric runs adversarial prompts and logs transcripts showing where the model ignored restrictions or disclosed system prompts.
• Value: Security teams can review exact transcripts to design better input sanitization and prevent exploitation.

 

2. Misinformation Generation (SF2)

• What it Measures: Whether the model produces factually incorrect or misleading responses.
• How Evidence is Collected: RiskRubric compares model outputs against trusted references and highlights deviations.
• Value: Developers and compliance officers gain visibility into hallucination risks, enabling fact-checking workflows or human review where needed.

 

3. Leakage of Personal Information (P1)

• What it Measures: Whether a model outputs sensitive personal data, either memorized or elicited during interaction.
• How Evidence is Collected: Probes test for exposure of personally identifiable information (PII), with annotated outputs tagged by severity.
• Value: Privacy teams can identify compliance risks early and decide whether to apply privacy-preserving techniques or limit deployment scope.

These examples show how RiskRubric bridges the gap between abstract risk scores and actionable evidence, enabling practical guardrail design and governance.

 

Call to Action: Help Shape the Future of RiskRubric

RiskRubric is the beginning of a global effort to create an open standard for AI model risk assessment. CSA is committed to ensuring that this standard is:

1. Community-Driven: We want your feedback on what features and capabilities should be prioritized in future versions of RiskRubric.
2. Standards-Aligned: We intend for the scoring rubric to be governed by CSA working groups and aligned with our AI Controls Matrix (AICM) and other key AI governance frameworks. This includes mapping RiskRubric indicators directly to AICM control objectives, ensuring that every piece of evidence collected can be tied to an actionable safeguard.
3. Extending to MCP: Our roadmap includes creating a version of RiskRubric for the Model Context Protocol (MCP). Leveraging CSA’s new MCP Security Control Center, this extension will allow us to scan MCP interactions and assess protocol-level risks, ensuring the methodology applies not only to models but also to the orchestration frameworks that connect them. If you are interested in participating, that will help us in setting a timeline for this.

For today, the best way to get involved is to join the CSA community in our public Slack channel. Here you can contribute ideas, ask questions, and help shape the future direction of RiskRubric.

👉 Join the RiskRubric Slack Channel

 

A Shared Responsibility for AI Security

The promise of AI will only be realized if we can build trust at every layer: models, applications, and infrastructure. RiskRubric and AICM together provide a roadmap for doing exactly that: empowering developers to innovate while giving security teams the tools to manage risk in real time.

We invite you to join us in turning RiskRubric into a community standard for model trustworthiness, ensuring that AI adoption is not only powerful but also safe, transparent, and aligned with human values.