The New AI Security Challenge

Generative AI is transforming the way businesses operate. From streamlining customer interactions to automating complex tasks, tools powered by large language models (LLMs) are quickly becoming essential across industries. AI Adopters have seen the incredible value these tools provide. An analysis from Mckinsey described around 63 use cases with the potential to generate $2.6T to $4.4 trillion in value across industries.

However, with innovation comes risk, and generative AI introduces a completely new set of security challenges that we cannot afford to ignore. Some of these risk factors include concerns around Hallucination, Cybersecurity, PII leaks, IP Infringement, and compliance challenges, among several other critical factors.

The Problem: A New Attack Surface

LLMs have inadvertently created a new attack surface. Generative AI systems, for all their power, can unintentionally leak sensitive data, expose intellectual property (IP), or even generate malicious outputs when exploited.

There are three main concepts as it relates to the intersection of Generative AI And Cybersecurity.

1.         Use of Gen AI by malicious actors to exploit vulnerabilities,
2.         Use of Gen AI by Cybersecurity companies to more efficiently protect vulnerabilities and
3.         The threats/attacks against Generative AI based systems (Chatbots and other applications).

While the first two are well-known, this article focuses on the third. Here are some of the most pressing risks that AI application builders and deployers will encounter:

•         Data Leaks: LLMs can reveal sensitive or proprietary information either embedded in training data or provided during interactions.
•         Malicious Code Generation: Models can be exploited to generate malware or phishing campaigns.
•         Compliance Risks: In regulated industries, outputs from LLMs can inadvertently violate laws like GDPR or HIPAA.
•         Prompt Injection Attacks: Crafty prompts can manipulate the AI’s behavior, leading to unintended or harmful outputs.

As these risks become more apparent, it is clear that traditional cybersecurity tools aren’t enough. Generative AI demands a new approach — a tailored solution to protect the data and systems it interacts with. A new way — an intelligent combination of capabilities provided by a gateway, proxy, a firewall, (WAF), and an integration strategy is the need of the hour for this rapidly evolving transformation.

DefendAI

DefendAI is a startup that is building that combination of capabilities specifically for generative AI applications and they recently released an initial version they call the Wozway. (https://github.com/Defend-AI-Tech-Inc/wozway) The overall DefendAI solution provides for a multi-layered defense system that addresses the unique threats posed by LLMs while allowing organizations to safely adopt these tools without compromising data security or regulatory compliance.

Here’s how it works:

1.         1. Adopters of AI Applications — whether allowing use of commercial LLM’s (OpenAI, Groq, Gemini, Anthropic, Perplexity) or building their own chatbots, quickly onboard their applications on to Wozway.
2.         2. Deploy protection policies with advanced capabilities — The controls can be deployed in a very finegrained configuration, applicable at model, applications and user based layers.
3.         3. Once enabled, the traffic is now routed through the Wozway that not only allows/alerts/blocks based on configured policies and protections but also provides for reports (dashboard, incident management and activities monitoring).

The DefendAI cloud is continuously updated with new threat research so the protection can be rapidly deployed, and the protection is always up to date.