Enabling AI. Feeding Enterprise Content

Introduction

In the digital transformation era, enterprises increasingly leverage artificial intelligence (AI) to enhance operations, increase productivity, and stay competitive. A recent Gartner study reveals that 72% of cybersecurity leaders view oversharing and exposing sensitive information to employees as the biggest risk.

Additionally, a recent survey indicates that employees with average permission levels can access over 17 million files on their first workday, leading to significant security and compliance risks. FileOrbis, a leading content management platform, provides a powerful AI enabler that allows organizations to utilize their private data to feed their enterprise AI systems without compromising privacy and security.

State of Feeding Content to AI Today

Content challenges for AI

  • Ensuring data accuracy, maintaining up-to-date data, and handling diverse content types.
  • Malicious actors can manipulate prompts to extract sensitive information or alter AI outputs.
  • Internal stakeholders with malicious intent can exploit their access to steal or manipulate data.
  • Inadvertent exposure of sensitive data without robust security measures.
  • Managing permissions across various content repositories can be overwhelming.
  • The absence of a unified file federation system complicates content management.
  • AI-driven content management can lead to unforeseen computing resources and data storage costs.

Why the need for responsible feeding to AI via Content Management

Responsible AI feeding ensures that AI systems have accurate, relevant, and secure data, enabling effective and efficient operations. Key benefits include:

  • Enhanced Security: Protects sensitive data from breaches and leaks through robust security measures.
  • Improved Compliance: Helps avoid legal and financial penalties by ensuring data management meets regulatory requirements.
  • Operational Efficiency: Automated workflows and intelligent permission management reduce human errors and streamline processes.
  • Cost Management: Controls operational costs by optimising AI resource usage.
  • Data Integrity: High-quality, up-to-date data ensures accurate and reliable AI outputs.
  • Scalability: A unified file federation and content management approach allows seamless operational scaling.

AI Enabler

FileOrbis is designed to be data repository agnostic, integrating seamlessly with various content sources such as NTFS/CIFS, FTP/SFTP servers, MS SharePoint, MS OneDrive, Google Drive, OpenStack Swift, Azure Blob Storage, AWS S3, and more. This broad compatibility ensures centralised data feeding processes, regardless of data location.

Key Features of FileOrbis AI Manager

A. Data Repository Agnostic Integration

  •  Ingests files from multiple data repositories, making it adaptable for diverse IT environments.
  • Allows access and utilization of relevant data for AI without complex migrations or reconfigurations.

B. Intelligent Filtering and Classification

  • Content-based intelligent filtering and classification select the most relevant data for AI processing.
  • Ensures data privacy and compliance, with personalized results based on permissions and aggregated results without compromising privacy.

C. Compliance Through Anonymization

  • Selective anonymization of sensitive data prevents unauthorized access and ensures compliance with data protection regulations.
  • Enables leveraging AI without compromising on data privacy and security.

D. Permission Check

  • Continuous permission management ensures only authorized personnel access specific data tailored to different roles and teams.
  • Maintains data integrity and security, which is crucial for sensitive enterprise environments.

Business Value of FileOrbis

  • Continuous Content Feeding: Ensures seamless and uninterrupted data flow for AI systems, maintaining operational efficiency.
  • Data Security: Prevents unauthorized access, mitigating risks of data breaches and ensuring compliance with security policies.
  • Integration with Content Repositories: Boosts productivity by integrating effortlessly with existing content and file repositories.
  • Risk Mitigation: Reduces exposure to insider threats and data disclosure incidents, maintaining a secure data environment.
  • Content Updates: Keeps AI prompts and models updated with the latest data, enhancing their relevance and accuracy.
  • Operational Cost Reduction: Minimizes the need for manual prompt and search activities, lowering operational costs.
  • Threat Elimination: Protects against prompt hacking activities, ensuring secure AI operations.

Conclusion

In conclusion, responsible AI enablement is vital for overcoming current content management challenges and ensuring AI systems deliver their full potential while maintaining security, compliance, and efficiency.