WEBINAR
In a world where AI is transforming how we work, staying ahead of data security risks has never been more critical. Now, companies can unlock the full potential of AI without worrying about data security, as AI agents reshape the way enterprises operate. With automation and enhanced efficiency, companies can overcome the complexity of managing sensitive data, ensure compliance, and address technical challenges.
At Polymer, we’ve developed Polymer’s SecureRAG, an AI-driven rule engine powered by LLM models, to solve these challenges. Polymer’s SecureRAG provides a scalable, secure, and flexible framework for enterprises deploying AI agents, ensuring maximum data security while maintaining operational efficiency.
Why enterprises need Polymer’s SecureRAG
AI agents rely on access to vast amounts of data, often sourced from diverse platforms like Slack, Google Workspace, Salesforce, and S3 buckets. This creates two major challenges:
- Technical complexity: There are common issues like data schemas varying significantly across platforms, inconsistent event logs and API data, and large-scale deployments often facing throttling and latency issues.
- Data security risks: Sensitive information could leak during AI model training or through prompts. Also, mismanagement of third-party access increases compliance and privacy risks.
Building and maintaining such infrastructure in-house is resource-intensive and prone to ongoing operational issues. Polymer’s SecureRAG eliminates these roadblocks by offering a robust, ready-to-deploy solution.
How Polymer’s SecureRAG works
Polymer’s SecureRAG addresses enterprise data security through two primary components:
1. Model input controls
Polymer’s SecureRAG classifies and governs every piece of data within your enterprise. By applying automated domain-level and information-level controls, Polymer’s SecureRAG ensures that only authorized data is accessible to AI agents. Key features include:
- Masking and redaction: Sensitive data fields (e.g., PII, PHI, or intellectual property) are obfuscated before AI processing.
- Metadata tagging: Documents are dynamically tagged with attributes like confidentiality levels or compliance requirements.
Example Use Cases:
- Healthcare and finance: Restrict data shared with AI agents to comply with strict industry regulations, avoiding catastrophic data leaks.
- Public cloud environments: Control data residency and ensure contractual compliance.
2. Dynamic access controls
Polymer’s SecureRAG provides real-time data access controls, enabling hyper-granular permissions. These controls ensure that only relevant data is visible, even during complex multi-agent interactions.
Example Use Cases:
- A helpdesk agent can access Zendesk tickets but redact sensitive fields for compliance.
- An onboarding agent can aggregate data from Google Drive and Outlook while adhering to user roles and compliance policies.
Plus, Polymer’s SecureRAG provides schema flexibility with access controls that are easily configured using attributes like user role, location, or actor type (human vs. non-human). This ensures scalability without compromising security or performance.
Frontier data: unlocking enterprise potential
“Frontier Data” refers to the core business information embedded in SaaS workflows—emails, documents, chat logs, and more. This data, when harnessed effectively, can transform AI use cases and drive enterprise innovation. If you want to dive deeper into this topic, be sure to check out our prior publication, “Frontier data’ from SaaS: An enterprise’s competitive advantage in leveraging AI,” where we explored how business heuristics can be gleaned from collaborative SaaS applications.
Polymer’s SecureRAG empowers enterprises to securely leverage this critical data to unlock its full potential, enabling:
- Unified Search Agents: Allow employees to seamlessly retrieve internal and external customer information across all platforms.
- AI-driven Customer Support: Train agents on Salesforce tickets and Slack conversations to enhance resolution times and improve customer experiences.
- Training & Onboarding Tools: Automate new hire onboarding with data sourced from HR systems, compliance documents, and team collaboration platforms.
The Polymer difference: Built for enterprise AI
Securing AI models is essential, but excessive restrictions can significantly reduce their effectiveness. Polymer’s SecureRAG overcomes this challenge by striking the perfect balance between security and performance.
- Flexible data governance: Fully customizable controls based on enterprise maturity.
- Real-time query audits: Logs and monitors AI interactions to identify risks and anomalies.
- Compliance tracking: Generates detailed reports for global regulatory adherence.
- Human risk management: Empowers business users to take an active role in data security by identifying and mitigating risks. Includes risk scoring to quantify human-based vulnerabilities.
- Real-time remediation: Automates risk reduction with inline tools such as redaction, filtering, blocking, link expiration, and keyword replacement—ensuring secure AI interactions at scale.
By combining model input controls with dynamic access controls, Polymer’s SecureRAG ensures both security and operational performance without compromise.
Balancing security and performance
With Polymer’s SecureRAG, enterprises gain a comprehensive framework for deploying AI agents securely and effectively. Key benefits include:
- Dynamic controls protect sensitive data without impacting AI outputs.
- Latency optimization minimizes delays, ensuring a smooth user experience.
Dynamic access controls play a critical role in achieving this balance. By enabling real-time control over data access, enterprises can protect sensitive information while maintaining output efficacy. Unlike static permissions, this scalable and flexible approach allows all relevant data to be ingested into the LLM model without compromising security. Even if sensitive data is part of a result, Polymer’s SecureRAG ensures it is never exposed.
Additionally, to optimize usability, latency management is prioritized during deployment. While maximum security can sometimes result in delayed response times, Polymer has designed its system to minimize these challenges. In internal tests, only 20% of prompts required further policy adjustments within the first month of deployment to address latency concerns. Non-human actors also are managed carefully to prevent unauthorized access caused by improper identity or access management (IAM) settings.
Through its dynamic controls and latency optimization, Polymer’s SecureRAG enables enterprises to deploy scalable, compliant AI systems without sacrificing usability or efficiency. By addressing risks upfront and fine-tuning policies as needed, enterprises can confidently adopt agent and chatbot solutions that deliver robust performance without compromising security.
Future-proof your AI strategy
As enterprises embrace AI, their data security needs will evolve. Polymer’s SecureRAG is designed to handle current requirements—like deploying Gemini for AI search—and future agentic use cases. Its flexible architecture integrates seamlessly with existing systems like Active Directory, Okta, or Google IAM, enabling centralized, enterprise-wide security.
Get started today
Contact Avery Neims at aneims@polymerhq.io for early access to Polymer’s SecureRAG and start securing your enterprise data in the age of AI.
