Comprehensive Governance, Risk, and Compliance Management for AI Models, Data, and Deployments
The Lorykeet “GRC for AI” Platform provides confidence in your use of AI being trustworthy, accountable, transparent, and valuable.
Centralized management of models and data catalog
Model assurance and audit enablement
Open platform allows for “bring-your-own” methods
Proactive risk management
Humans in the loop
Overcome resistance and accelerate adoption
Our technology allows you to test and deploy machine learning models that are trustworthy and actually trusted
Building trust into and earning trust in your AI
There are two areas where trustworthiness is key to AI actually delivering value. The first is adoption by the intended users. “While the value of artificial intelligence is now undoubtable, the question has become how to best use it—and that often boils down to how much workers and end users trust AI tools.” – Deloitte 2023 Tech Trends. The second area is meeting risk management, customer needs, and compliance requirements. As McKinsey points out in its September 2022 article Why Businesses Need Explainable AI and How to Deliver It, “Explainability helps organizations mitigate risks. AI systems that run afoul of ethical norms, even if inadvertently, can ignite public, media, and regulatory scrutiny.” Lorykeet’s platform has been designed and developed to address both of these needs. Speed up AI adoption with AI outcomes, algorithms and predictions you can understand, explain, and in which you can earn trust. Reduce bias and discrimination, and achieve fairness goals. Demonstrate regulatory compliance.
Centralized ML Models + Data Catalog
Effective AI Governance Requires Enterprise-level Controls
Managing Al risk and compliance is an enterprise challenge. AI silos hinder trust and create unnecessary fragmentation and complexity. Lorykeet provides a unified view of ML models and Data across all key stakeholders using “intelligent model and data discovery.” Machine learning models are 90% data and 10% code. Knowing what data goes into which model is critical to tracking model inventory and performance across the AI lifecycle. Centralized documentation drives visibility into enterprise-wide Al risk exposure and compliance assurance. Lorykeet becomes your system of record for AI. With our unified, explainable AI platform you enable centralized and standardized Al governance effort.
Humans in the Loop for all phases of the AI lifecycle
Responsible AI Requires Collaboration between Artificial and Human Intelligence
Humans-in-the-loop typically refers to bringing together AI and human intelligence to create machine learning models. Humans are involved with setting up the systems, tuning, and testing models so prediction and decision-making improves. Human in the Loop aims to achieve what neither a human being nor a machine can achieve on their own. Lorykeet enables humans in the loop to achieve good governance, risk management and compliance. The platform is designed to facilitate human “stakeholders” interaction with the models, data, and results and allow validation of outcomes continuously. At a time when vendors focus on tools for data scientists, Lorykeet’s approach fosters collaboration among data scientists, business experts and risk analysts to proactively manage and govern AI risks. The result is AI that is fully vetted and trusted for business adoption, and able to stand up to regulatory, policy and risk reviews.
Supporting objective and independent analyses and audits
BYOM with Lorykeet’s Open & Comprehensive Platform
As AI deployments expand and grow, managing compliance risks will increasingly require objective and independent reviews. Model assurance will become an important element in organization’s effectively and responsibly developing and using AI. Anticipating this need, Lorykeet has been designed to support multiple ML frameworks and ML platform, as well to enable the use of multiple XAI, bias/fairness test methods. Clients can easily add external ML model testing & validation methods, aka BYOM - Bring Your Own Method.