Data breaches within U.S. government agencies resulted in an estimated $26 billion in losses over the past eight years, with incidents such as those at the U.S. Postal Service and the Office of Personnel Management (OPM) exposing millions of sensitive records. While artificial intelligence (AI) is being adopted across federal operations, a study from the GAO highlights uneven implementation—NASA leads with 390 AI use cases, while agencies like the OPM and EPA report only 1 to 4 cases, underscoring the urgent need for AI-driven solutions to enhance data security and operational efficiency.
One of the most promising advancements is Retrieval-Augmented Generation (RAG), a technology that integrates real-time data with AI-generated responses, improving accuracy and relevance. For federal agencies, particularly in critical sectors like healthcare, RAG offers the potential to bridge the gap in AI adoption, providing more secure and compliant solutions. TechSur’s RAG as a Service addresses the unique challenges government entities face, ensuring both data integrity and compliance with regulatory frameworks.
Limitations of Current Large Language Models (LLMs)
Large Language Models (LLMs), such as GPT-3 and GPT-4, excel at generating human-like text based on patterns learned from extensive datasets. However, they face significant limitations in government settings, particularly in handling sensitive, real-time data. These models are trained on static data, so their knowledge is often outdated. This leads to inaccuracies when responding to queries that require current information. Furthermore, LLMs are prone to generating “hallucinations”—responses that seem plausible but are incorrect or nonsensical. In federal agencies, such hallucinations can result in misinformation, impacting decision-making processes and undermining trust in AI systems.
Additionally, LLMs lack transparency and traceability in their reasoning processes, making it difficult to understand how they arrive at certain conclusions. This “black box” nature poses risks for government agencies that must ensure accountability, data integrity, and compliance with regulatory standards. LLMs are unsuitable for critical applications like healthcare, security, and policy-making without appropriate safeguards, such as data validation mechanisms and contextual grounding. These limitations highlight the need for advanced solutions like RAG to mitigate risks and improve accuracy in government AI deployments.
Understanding Retrieval-Augmented Generation (RAG)
RAG enhances generative AI by connecting it to external data sources, providing real-time context to improve the accuracy of responses. By integrating structured data through taxonomies, ontologies, and knowledge graphs, RAG helps AI models generate more accurate, contextually relevant responses and reduces hallucinations. Key components of RAG include:
- Contextual Data Enrichment: Leverages organization-specific taxonomies to provide AI with deeper contextual understanding.
- Knowledge Graphs: Organizes and uncovers connections within data, ensuring that AI responses are based on precise information.
- Prompt Enhancement: Frames user queries using knowledge graphs, ensuring precise and context-aware answers.
- Response Validation: AI outputs are validated against knowledge models. This is to ensure accuracy and reliability.
Challenges of Implementing LLM in Government Settings
Implementing LLM in government settings presents unique challenges. This is mainly due to the sensitivity of classified data and the need for strict access controls. Federal agencies require paragraph-level data classification rather than document-level to ensure only authorized information is accessed. This significantly complicates AI deployment. Moreover, robust security features like advanced access control, auditing, and monitoring are essential to meet regulatory compliance standards. Commercial RAG solutions for enterprises often need more fine-grained control and security features for government applications. This gap highlights the need for specialized RAG solutions delivered as a service tailored to meet federal agencies’ rigorous data protection and compliance requirements of federal agencies.
TechSur’s OnyxAI: Addressing Government-Specific Needs
Developing tailored RAG solutions is crucial to bridge the gap between commercial AI solutions and government-specific needs. TechSur’s OnyxAI offers:
- Enhanced Data Classification and Stringent Aggregate Control: Provides fine-grained, paragraph-level control to manage sensitive information precisely, ensuring that only authorized data segments are accessed. Combining it with comprehensive oversight aligned with federal security policies allows agencies to monitor and protect data more effectively.
- Regulatory Alignment and Compliance: This includes built-in frameworks that ensure adherence to government-specific regulations. Automated compliance checks to continually assess and reduce the non-compliance risks, maintaining accountability throughout operations.
- Mitigating Security Risks: Utilizes Access Control Lists (ACLs), which restrict access to classified data, ensuring that only authorized personnel can view or modify sensitive information. This security feature helps prevent unauthorized data access and breaches.
- Seamless Integration: Fully compatible with common platforms like Microsoft Teams and Slack, making it easy for federal agencies to securely access and share data across their existing communication systems. Along with this, they can ensure compliance with government data protection standards.
How Potential Customers Use OnyxAI
Customers can leverage OnyxAI to improve decision-making and operational efficiency by integrating real-time external data with generative AI. This is especially valuable in sectors with stringent regulatory standards, such as immigration and healthcare.
Federal Healthcare as a Use Case
The Center for Medicare and Medicaid Services (CMS) is working to modernize healthcare delivery by integrating advanced health IT solutions. RAG can play a key role by augmenting clinical decision support systems. For instance, GPT-4 turbo combined with RAG has been used to manage bipolar depression by integrating clinical guidelines and evidence-based data in real-time, improving diagnosis and treatment recommendations. This application has shown how RAG enhances both the specificity and accuracy of AI responses. It efficiently reduces errors in critical healthcare decisions. This serves as a model for broader RAG adoption across other agencies, allowing for accurate data retrieval and improved decision-making in complex and regulated environments.
Conclusion
TechSur’s RAG as a Service provides federal agencies with a secure, compliant, and efficient AI solution. It is the right choice for enhancing decision-making and data management. Integrating real-time data and automating processes, it addresses the unique challenges government entities face.
Contact us today to learn how OnyxAI can transform your agency’s operations with cutting-edge AI solutions.