Use Case: Predict product demand across regions and time periods using AI/ML models that integrate internal and external data sources, while maintaining strong data security, RBAC, and policy governance.
Understand how Forecasting is enabled for your organization..
AI Security starts with good security hygiene.
✔️ Fix your vulnerabilities
✔️ Enforce Zero Trust, SSO, and RBAC
✔️ Classify your data properly
✔️ Log all prompts, model actions, and agentic decisions
The fundamentals haven’t changed — the technology may look new, but the security goals remain the same.
Get the basics right before chasing the buzzwords.
Data Driven AI Security Controls
Classify Data– In most cases Forecasting data will be considered “Restricted” or Highly Confidential”
your forecasted data will require RBAC + ABAC, Security Logging and Monitoring for your Prompts
A planner can query:
“What’s the Q4 forecast for Product X in the Southeast region, and what factors drive the change from last quarter?”
LLM Response (securely scoped):
“Forecasted demand for Product X in Southeast increased by 12% due to promotional uplift and improved lead time reliability. Based on your access level, financial margin data is masked.”
Authorization Flow
- User authenticates via Okta → Authenticate
- Role resolved in SailPoint → RBAC Authorization
- Authorization policy enforced via PlainID → Data Level Security Authorization
- Query generated →
- Forecast data retrieved from Snowflake →
- Secure output shown in UI or chatbot.
- LLM interprets and explains results →
Leave a Reply