The AI Playbook You’ve Been Waiting For: Operating Models for AI Guardrail Creation, Evaluation, and Monitoring
The Dynamo AI team is continually asked about how to best implement security and compliance controls to streamline the productionization AI use cases.
- How does our organization organize its operating model to test and evaluate AI?
- How do we avoid the pitfalls that other organizations have suffered when validating the performance of their AI controls?
- How do we resource for AI observability?
- How do we know what metrics to provide management, audit, and the regulators?
What those implementing AI continue to ask for is granular, best practices to drive the AI test, evaluation, and guardrail controls Dynamo's AI, engineering, and risk management experts have deployed across the regulated enterprises.
That is why Dynamo AI is excited to announce our series of Dynamo AI Playbooks, the first in our series focusing on end-to-end insights across the creation, evaluation, and monitoring of AI guardrails. Dynamo AI Playbooks include comprehensive detail for AI leaders to pick up and live by as they deploy effective AI controls across their enterprise. Each playbook covers essential operating model guidance including:
- Process steps and implementation detail for each core component of the AI control lifecycle covered
- Guidance on common pitfalls to avoid when implementing your AI controls
- Roles and responsibilities recommendations, including guidance on resource estimation
- Key metrics and decision points throughout the operating model lifecycle
- Alignment to the broader AI implementation lifecycle
- Clear success criteria objectives and management reporting guidance
The first playbook in our series, the Dynamo AI Guardrail Playbook, dives deep into the creation of AI guardrails, which allow organizations to mitigate privacy, security, and compliance risks when engaging with AI systems. The Playbook provides in-depth operational content on the creation, evaluation, and monitoring components of AI guardrail implementation, delivering Dynamo's expertise through each of the sub components listed below.
Dynamo has seen that enabling guardrails effectively, with minimal effort and faster time to market, allows enterprises to confidently scale AI. Avoiding the blockers others have faced propels AI leaders and empowers enterprises to scale. What are just some of the guardrail implementation landmines we are looking out for?
- Organizations starting to develop policy-based guardrails without understanding the fundamentals to consider in their definitions, how to achieve effective coverage, or what to look out for when testing their guardrail definitions.
- A focus heavily weighted on safety and toxicity during the guardrail development, without proper assessment of compliance risk considerations.
- Attempts to scale manual red-teaming of models and guardrails without an effective plan to transition to an automated solution, with growing risks and costs.
- Unexplained automated red-teaming without clear metrics and context to convey the risks to leadership and non-technical stakeholders.
- Minimal planning and processes in place for guardrail monitoring, including misleading or inaccurate metrics on the results.
- Forgetting to continually assess guardrail performance drift, including key metrics to look out for to ensure effective system performance.
Dynamo AI is committed to maintaining these Playbooks as the implementation of AI controls matures, and has established an internal, customer, and marketplace mechanisms to do so, ensuring our guidance remains relevant, cutting edge, and practical.
Reach out to Dynamo AI for your Playbook here, and look out for the next in our series as we continue to advance the enablement of safe, secure, and compliant AI.
