Blog Article

Two big Buckets of Security: AppSec and IT Security

Arnav Bathla

8 min read

In the rapidly evolving landscape of enterprise technology, the adoption of Large Language Models (LLMs) has marked a significant leap forward. While LLMs bring unparalleled capabilities in processing and generating human-like text, they also introduce complex security challenges that enterprises must navigate. This blog post delves into the dual aspects of security challenges posed by LLM integration—Application Security (AppSec) and IT Security.

The Dual Dimensions of LLM Security Challenges

The integration of LLMs into enterprise systems presents a multifaceted security landscape, encompassing both AppSec and IT Security challenges. Understanding these challenges is the first step toward developing robust security measures.

Application Security (AppSec) Challenges

AppSec focuses on protecting the software and applications from threats and vulnerabilities, particularly when integrating with 3rd party LLM APIs. The major challenges include:

  • Data Leakage: The risk of exposing sensitive PII or proprietary IP through interactions with LLMs.

  • Prompt Injection Attacks: The threat of malicious inputs designed to elicit unauthorized responses from LLMs.

  • API Vulnerabilities: Security weaknesses in the API layer that connects applications to LLM services.

  • Compliance Risks: Ensuring application interactions with LLMs adhere to regulatory and compliance standards.

  • Hallucination: Providing incorrect outputs is one of the biggest challenges when working with LLMs. Same prompt can result in widely different outputs.

IT Security Challenges

IT Security, on the other hand, deals with the broader aspects of technology management within an organization, especially when employees use third-party LLM applications. Key concerns are:

  • Unauthorized Access: Preventing unauthorized access to LLM applications and ensuring secure authentication practices.

  • Network Security: Securing the data in transit to and from LLM applications against interception and manipulation.

  • Data Privacy and Governance: Ensuring no sensitive data is revealed to a model via a 3rd party LLM app such as ChatGPT. Maintaining control and governance over the data processed by LLM applications to prevent misuse.

  • Monitoring and Compliance: The need for continuous monitoring of LLM application usage to ensure compliance with security policies and regulations.

Navigating the Security Maze

The most important thing a team can do is to ensure they're being proactive instead of taking a reactive approach to LLM security. Based on a recent study, here are some fascinating stats that can help you understand why it's important to be proactive about this:

In all, 56% of respondents are using generative AI tools for work tasks.

  • 31% report using generative AI on a frequent, regular basis—including daily (9%), weekly (17%), or monthly (5%).

  • 25% say they are using generative AI occasionally.

  • 44% have ne­ver used generative AI.

Among workers who've adopted generative AI, a large majority—71%—say their managers or organizations are aware of their usage.

  • 46% say management is fully aware of their AI use.

  • 25% say management is partially aware.

  • Just 13% say their managers are not aware.

Most respondents—63%—say generative AI tools have positively impacted their productivity.

  • 7% report a significant increase in productivity.

  • 56% report an increase.

  • 36% report no impact.

Securing the AppSec Layer

Our approach to AppSec involves a specialized security layer that detects and masks potential data leakage in real-time. This layer acts as a safeguard, analyzing data flows to identify sensitive information and automatically masking it before exposure. Key benefits include:

  • Real-time Protection: Immediate detection and masking of sensitive data, ensuring secure interactions with LLM APIs.

  • Seamless Integration: Designed to work smoothly with various LLM APIs, Layerup is LLM agnostic.

  • Compliance Assurance: By preemptively securing data, we help organizations meet strict regulatory requirements, safeguarding against compliance risks.

Securing the IT Security Layer

To address IT Security challenges, Layerup offers a state-of-the-art browser extension that empowers IT teams to monitor and control the use of third-party LLM applications. This tool enables:

  • Governance: Comprehensive visibility into how every single employee is using 3rd party LLM applications and how these LLM apps are accessed and utilized within the organization.

  • Policy Enforcement: The capability to set and enforce usage policies directly, ensuring alignment with organizational security standards.

  • Prevent data leak: Governance, blocking PII or sensitive data from going to 3rd party models.

  • Alert System: Immediate notification of any unauthorized or suspicious activity, facilitating prompt response to secure the enterprise environment.

Conclusion

As LLM technologies continue to transform enterprise operations, security remains a paramount concern. If you'd like to work with a partner that can stay on top of all the up-and-coming threat vectors and ensure we're safely moving towards a great future of secure AI adoption, then book a demo with us.

Application Security for Generative AI

arnav@layerupai.com

+1-650-753-8947

Subscribe to stay up to date with an LLM cybersecurity newsletter:

Application Security for Generative AI

arnav@layerupai.com

+1-650-753-8947

Subscribe to stay up to date with an LLM cybersecurity newsletter:

Application Security for Generative AI

arnav@layerupai.com

+1-650-753-8947

Subscribe to stay up to date with an LLM cybersecurity newsletter: