What rollback and comms plan should we prepare in case the Rise Calendar roll-out causes performance regressions?

To prove the Tilda update improved team productivity without increasing risk, you need to track and balance two sets of Key Performance Indicators (KPIs): productivity metrics (related to delivery speed and efficiency) and risk metrics (related to quality and reliability). Evaluating them in combination provides a holistic view of the update's true impact.

As a general best practice, you should avoid enabling new Dependabot features during any planned or recurring change-freeze windows for your organization. These periods are designed to minimize risk and maintain system stability during times when IT support is limited or business operations are critical.

To minimize help-desk spikes from a major September release, segment the rollout using a phased, risk-based approach with robust monitoring and a comprehensive communication plan. Instead of a single big bang release, use feature flags, canary deployments, and user segmentation to introduce changes gradually and control the blast radius of any issues.

To prevent sensitive data from being pasted into embedded AI assistants in office apps, organizations must enforce a combination of Data Loss Prevention (DLP) policies, access controls, and data classification. Tools like Microsoft Purview can implement and manage these controls at a granular level. 
Policy creation with Microsoft Purview

• Data classification
• Data Loss Prevention (DLP) rules
• Access controls and endpoint management
• Additional controls

To prove the provenance of AI-generated documents in a legal context, watermarking must be implemented consistently across different suites and document formats. The strongest method combines invisible, embedded watermarks from the AI model itself with a robust, metadata-based provenance chain managed through a blockchain or secure database. 
The approach has two key components: Generative watermarking and a trusted content provenance system.

• 1. Generative watermarking

This embeds a subtle, imperceptible signal into the content as it is being created by the AI model. For legal purposes, this is more robust than a post-generation watermark that can be easily removed.

• 2. Trusted content provenance system

This method securely records metadata and proof of origin, independent of the content itself. The most robust implementations use blockchain technology to create an immutable and verifiable record.