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Securityintelligence
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Best practices on securing your AI deployment
- Organizations are adopting generative AI to gain efficiency and productivity gains and to improve innovation in products and services,
- However, securing these AI projects is essential to ensure trustworthy AI - 82% of executives agree but only 24% of generative AI projects are secured,
- Organizations face challenges to securing AI deployment, as generative AI creates a new type of data store that can create new data based on organizational data which can have PII & sensitive information. At the same time, attacks targeting machine learning models have a high propensity to cause risk or damage,
- IBM’s framework for securing AI revolves around securing the data, model, usage and infrastructure. These security efforts should include AI governance frameworks that monitor fairness, bias and drift over time,
- To address securing data, IBM Guardium AI Security allows organizations to identify and fix vulnerabilities in the AI model and protect sensitive data. Organizations have access to the IBM Guardium Data Security Center which empowers security and AI teams to collaborate across the organization through integrated workflows and centralized compliance policies,
- AI security is a collaborative process across cross-functional teams, such as security teams, risk and compliance teams, and AI teams,
- IBM Guardium AI Security helps to continuously monitor for AI misconfiguration, detects data leakage and optimizes access control,
- Securing AI deployment requires organizations to consider regulatory compliance from the data collection and handling phase to model development and inferencing,
- Webinars, such as IBM's Guardium AI Security, help organizations understand how they can implement secure and trustworthy AI projects,
- IBM's Guardium AI Security offers comprehensive and continuous support for organizations to achieve trustworthy AI projects in a safe and reliable manner.
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