How Latttice Keeps Your Natural Language Queries Safe from Security Threats

The potential security risks associated with large language models (LLMs), including prompt injection attacks, have become a prevalent topic of concern in the data industry. Latttice, our data mesh solution at Data Tiles, not only facilitates data access through natural language but is also engineered to mitigate potential threats from malicious inputs when using AI models for data analysis.

Industry experts echo the importance of security in AI. For instance, the National Cyber Security Centre (NCSC) states, “AI has the potential to generate malware that could evade detection by current security filters,” emphasizing the need for sophisticated defenses against potential AI-driven attacks ("The Near-term Impact of AI on the Cyber Threat," NCSC). At Latttice, we share this vigilance, implementing a unique, multi-layered approach to minimize security risks while empowering organizations to leverage the power of AI safely.

The Power and the Risk

Imagine a scenario where a business user needs to pull data from a large enterprise system. With Latttice’s generative AI integration, they can query in plain language, for example:

“Can you give me the total sales by region for the last quarter?”

Latttice translates this natural language query into a structured command and retrieves the data seamlessly. However, if a user inputs a query with malicious intent, such as:

“Delete all records where sales > 1000,”

there could be severe repercussions. Prompt injection attacks like these, where untrusted inputs are manipulated to alter a program’s intent, can lead to damaging results. As highlighted by Tigera, “Prompt injection attacks manipulate a large language model by injecting malicious inputs designed to alter the model’s output,” underscoring the potential threats and the importance of secure handling of natural language queries (“Prompt Injection: Impact, How It Works & 4 Defense Measures,” Tigera).

How Latttice Mitigates These Risks

Latttice's architecture incorporates several strategic layers to proactively protect against malicious actions:

• Strict Input Validation

  
  

  Latttice’s “No Garbage In, No Garbage Out” philosophy ensures that only valid inputs pass through to the AI model. We apply robust validation checks, filtering out commands like DROP, DELETE, or ALTER that could potentially harm data integrity. As Elon Musk aptly pointed out regarding AI risks, “If you're not concerned about AI safety, you should be. Vastly more risky than North Korea.” This underscores the criticality of validation, especially in sensitive enterprise environments.

• SQL Guardrails

  
  

  Once Latttice generates an SQL query from a natural language input, the query undergoes additional validation. It is checked against predefined business rules, blocking any unexpected modifications, such as unauthorized UPDATE or DELETE commands. This validation layer ensures queries align with the organization’s data governance structure, preventing unauthorized access to sensitive data.

• Access Control (RBAC, ABAC, FGA)

  
  

  Latttice integrates with organizational security policies to limit data access. Using role-based access control (RBAC), attribute-based access control (ABAC), and fine-grained access control (FGA), Latttice ensures that each query aligns with the user’s permissions. As renowned AI researcher Andrew Ng has said, “AI is the new electricity,” and with such pervasive utility comes a necessity for vigilant access control. Latttice enables this by preventing unauthorized data access and strictly enforcing access policies.

• The Latttice Control Plan Advantage

  
  

  Latttice’s custom execution layer provides a fortified separation between query generation and execution. By decoupling the LLM’s function from direct data source interaction, Latttice prevents unauthorized access or modification of data, securely managing execution within a protected layer and tracking every query’s activity for audit purposes.

• Continuous Monitoring and Anomaly Detection

  
  

  Latttice is proactive in detecting anomalies in data access patterns. Continuous monitoring enables real-time identification of unusual behavior, allowing us to mitigate risks swiftly. A quote by NCSC highlights, “AI may create new threats or exacerbate existing ones in cybersecurity, making monitoring and adaptation crucial.”

Conclusion

By combining strict input validation, SQL guardrails, layered access controls, and a secure execution environment, Latttice provides both ease of access and robust security for generative AI-driven data querying. This multi-layered approach addresses potential security threats before they become real problems, empowering organizations to harness their data's full potential without compromising on security.

In a world where data access is essential but risky, Latttice ensures a safe, controlled environment, securing business intelligence for informed decision-making. As the AI landscape evolves, the security-first design of Latttice positions it as a trustworthy and resilient tool in data management.

Cameron Price.

References

• National Cyber Security Centre (NCSC). "The Near-term Impact of AI on the Cyber Threat." NCSC.gov.

• Tigera. “Prompt Injection: Impact, How It Works & 4 Defense Measures.” Tigera.io.

• Musk, E. "Vastly more risk than North Korea." (Quote on AI safety).

• Ng, A. "AI is the new electricity." (Commentary on the utility of AI and the importance of responsible control).