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AI is accelerating faster than most enterprise organizations have the ability to govern it safely. In the rush to deploy increasingly sophisticated AI systems, a critical gap has emerged between innovation and risk management. In partnership with Cloud Security Alliance (CSA) we’re excited to announce RiskRubric.ai, the industry’s first tool to deliver standardized, comprehensive risk assessments for AI models at scale.
The Trust Gap in AI Adoption
The landscape of AI deployment has fundamentally shifted. What began as simple chatbot implementations has evolved into complex agentic AI ecosystems where large language models are granted unprecedented access to enterprise tools, sensitive databases, and have autonomous decision-making capabilities. Organizations are essentially handing AI systems the keys to their digital kingdom, often without fully understanding the risks involved.
This evolution has created two interconnected challenges that plague every AI-forward organization. First, there’s the model selection dilemma: How do you embed meaningful security and risk considerations into the process of choosing which AI models to deploy? Second, there’s the assurance problem: How do you confidently communicate to leadership, clients, and stakeholders exactly what risks your organization faces from current AI implementations?
The operational friction is painfully real. Engineering teams find themselves stalled by guesswork, waiting weeks for model approvals while trying to navigate inconsistent review processes. Security teams face mounting pressure to greenlight technologies they can’t fully evaluate, often lacking the specialized knowledge needed to assess AI-specific risks. Meanwhile, business leaders demand faster AI adoption while simultaneously requiring ironclad assurances about safety and compliance.
Why Traditional Risk Management Fails
Legacy risk assessment frameworks were designed for a different era, one where technology evolved predictably and security vulnerabilities followed established patterns. The breakneck pace of AI development has rendered these approaches obsolete. New models launch weekly, capabilities shift dramatically between versions, and the attack vectors are unlike anything we’ve seen before.
But there’s a deeper, more fundamental problem: these models are black boxes. Most commercial AI systems operate with closed weights, opaque training data, and inherently non-deterministic outputs. Traditional security tools can’t peer inside to understand what the model learned, how it makes decisions, or what failure modes might emerge under stress. We’re essentially flying blind, trying to assess the safety of systems we can’t fully inspect.
The few risk assessment tools that do exist are either prohibitively expensive, require deep AI expertise to interpret, or produce inconsistent results across different models. What the industry desperately needs are clear, standardized, fast-to-consume assessments that empower non-specialists, CISOs, compliance officers, business leaders, to make informed risk decisions without requiring a PhD in machine learning.
Introducing RiskRubric.ai: AI Risk Scorecards That Scale
RiskRubric.ai addresses this gap head-on. We’ve partnered with industry leaders to help build the industry’s first platform to deliver structured, pillar-based risk grades across 40+ AI models, with the depth and rigor that enterprise security demands but the clarity that busy executives need.
Here’s what powers our assessments:
Models Covered: We evaluate more than 150 models and are growing, with monthly updates as new versions are released. From GPT-4 and Claude to specialized models like Llama and Gemini, we cover the models your teams actually want to use.
Testing Depth: Each model undergoes comprehensive evaluation through 1,000+ reliability prompts designed to test consistency and behavior across diverse scenarios. We conduct 200+ adversarial prompts specifically crafted to probe for security vulnerabilities. Our automated systems perform full code scans of any downloadable model components, and our research team conducts thorough reviews of model cards and documentation.
Scoring System: Every model receives 0–100 scores across six independent “Risk Pillars,” which roll up into final letter grades (A–F). But we don’t stop at scores, each assessment includes actionable remediation insights that your teams can implement immediately.
Our six pillars provide comprehensive coverage of AI risk:
Transparency: We meticulously check published data sources, training disclosures, and license terms. Does the model provider clearly document what data was used for training? Are there licensing restrictions that could impact your use case? Is the model’s development process adequately disclosed?
Reliability: We run extensive repeatability suites and measure output variance across identical prompts. Can you count on this model to behave consistently? Does it handle edge cases gracefully? Will it maintain performance under real-world conditions?
Security: We red team specialists test for prompt injections, jailbreak attempts, and hostile code execution scenarios. We probe for vulnerabilities that could allow attackers to manipulate model behavior or extract sensitive information from your systems.
Privacy: We evaluate models’ propensity to request personal data, inspect data retention policies, and probe for potential training-data leakage. Could this model inadvertently expose confidential information? Does it handle PII appropriately?
Safety: We systematically test harmful-content filters using structured adversarial prompts designed to bypass safety mechanisms. We evaluate the model’s resistance to generating dangerous, biased, or inappropriate content across various domains.
Reputation: We track each model’s history, monitoring how it has improved (or degraded) over time, analyzing security incidents, and assessing the provider’s track record for responsible AI development.
What Makes This a Game-Changer for CISOs and AI Governance Teams
RiskRubric.ai transforms AI risk management from reactive guesswork into proactive, data-driven decision making. Instead of spending weeks researching individual models or relying on vendor marketing materials, security leaders can access comprehensive risk profiles in minutes.
Actionable insights at a glance mean no more ad-hoc decisions based on incomplete information. Our standardized scoring system allows you to quickly compare models across your organization’s specific risk tolerance levels.
Faster approvals and clearer stakeholder communications become possible when you can present concrete, objective risk data to leadership. Instead of saying “we think this model might be safe,” you can say “this model scored 92/100 on security with specific strengths in X and recommended mitigations for Y.”
Objective, audit-ready documentation for every assessed model means compliance teams have the evidence they need to demonstrate due diligence. Our reports provide the paper trail that auditors and regulators increasingly expect.
Easy alignment with organizational risk tolerance happens naturally when risk data is presented consistently across all models. You can establish clear policies like “only deploy models with B+ grades or higher” and have confidence those decisions are based on rigorous, standardized assessment.
Ready to Transform Your AI Risk Management?
Today marks the official launch of RiskRubric.ai and comprehensive AI Model Scorecards. We’re making it easier than ever for organizations to deploy AI confidently and responsibly.
Explore our existing model reports to see how your current AI implementations stack up against our risk framework. You might be surprised by what you discover, both positive and negative.
Join our community by suggesting new models for evaluation, requesting deeper analysis on specific risk areas, or providing feedback on our methodology. AI risk management is a collective challenge that requires collective solutions.
Access core ratings for free as part of our commitment to raising the bar for AI safety across the industry. More advanced enterprise features, including detailed test logs, API integrations, and custom policy alignment, are coming soon for organizations that need deeper capabilities.
The age of deploying AI on trust alone is over. The age of evidence-based AI risk management begins now. At Noma Security we are committed to ensuring the security of AI is as easy and accessible as the technology itself. This was a tremendous effort from the team and we look forward to partnering with more key initiatives in the future.
