Digital trust with partners requires embedding privacy considerations into their entire lifecycle, from onboarding to system access. This proactive approach builds confidence and prevents data breaches within the extended enterprise, rather than treating privacy as a reactive compliance task.
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To succeed, marketers must stop passively accepting the data they're given. Instead, they must proactively partner with IT and privacy teams to advocate for the specific data collection and governance required to power their growth and personalization initiatives.
Enabling third-party apps within ChatGPT creates a significant data privacy risk. By connecting an app, users grant it access to account data, including past conversations and memories. This hidden data exchange is crucial for businesses to understand before enabling these integrations organization-wide.
Security and user experience efforts often focus on employees and customers, but research reveals that almost 50% of users accessing corporate data are external. This massive, overlooked user base represents a significant security and productivity blind spot for most organizations.
Data governance is often seen as a cost center. Reframe it as an enabler of revenue by showing how trusted, standardized data reduces the "idea to insight" cycle. This allows executives to make faster, more confident decisions that drive growth and secure buy-in.
Brands must view partner and supplier experiences as integral to the overall "total experience." Friction for partners, like slow system access, ultimately degrades the service and perception delivered to the end customer, making it a C-level concern, not just an IT issue.
To test complex AI prompts for tasks like customer persona generation without exposing sensitive company data, first ask the AI to create realistic, synthetic data (e.g., fake sales call notes). This allows you to safely develop and refine prompts before applying them to real, proprietary information, overcoming data privacy hurdles in experimentation.
As AI personalization grows, user consent will evolve beyond cookies. A key future control will be the "do not train" option, letting users opt out of their data being used to train AI models, presenting a new technical and ethical challenge for brands.
Treating AI risk management as a final step before launch leads to failure and loss of customer trust. Instead, it must be an integrated, continuous process throughout the entire AI development pipeline, from conception to deployment and iteration, to be effective.
Instead of managing individual external users, host organizations should provide partners with user-friendly tools to manage their own team's access. Partners have better "intimacy" regarding who has joined or left, allowing them to revoke access promptly and reduce risks like orphaned accounts.
The modern security paradigm must shift from solely protecting the "front door." With billions of credentials already compromised, companies must operate as if identities are breached. The focus should be on maintaining session security over time, not just authenticating at the point of access.
