𝗛𝗼𝘄 𝗧𝗼 𝗚𝗼𝘃𝗲𝗿𝗻 𝗔𝗽𝗽 𝗟𝗲𝘃𝗲𝗹 𝗔𝗜 𝗜𝗻 𝟰 𝗣𝗵𝗮𝘀𝗲𝘀

𝗛𝗼𝘄 𝗧𝗼 𝗚𝗼𝘃𝗲𝗿𝗻 𝗔𝗽𝗽-𝗟𝗲𝘃𝗲𝗹 𝗔𝗜 𝗜𝗻 𝟰 𝗣𝗵𝗮𝘀𝗲𝘀

AI features now live inside the tools you use every day.

They sit inside your CRM, your coding tools, and your email. This creates shadow AI. Employees use these features without IT knowing. This leads to data leaks and security gaps.

You cannot ban AI. You must govern it.

Use these four phases to manage AI risk:

Phase 1: Discovery You cannot fix what you cannot see.

• Audit every application for embedded AI.
• Use network monitoring to find connections to AI services.
• Ask your teams which AI tools they use.
• Group these features by what they do, like code generation or data analysis.

Phase 2: Risk Assessment Decide what is safe and what is not.

• Map how data flows into these AI tools.
• Check if the AI uses your data to train its models.
• Form a committee with legal, IT, and security leaders.
• Create clear rules on what employees can and cannot do.

Phase 3: Implementation Turn your rules into technical controls.

• Use built-in settings to turn off risky AI features.
• Deploy data loss prevention tools to stop sensitive info from leaving.
• Give your developers safe, approved AI environments to work in.
• Set up response plans for AI data leaks.

Phase 4: Continuous Monitoring AI changes fast. Your rules must change too.

• Watch API usage and data patterns in real time.
• Run regular audits to check for compliance.
• Train your staff on how to use AI safely.
• Update your policies as new tools emerge.

Treating AI as a side issue creates massive risk. A phased approach turns reactive damage control into a proactive strategy.

Source: https://dev.to/autonainews/how-to-govern-unsanctioned-app-level-ai-in-4-enterprise-phases-3lfd

Optional learning community: https://t.me/GyaanSetuAi