CPX 2024: AI in Action at Check Point

Check Point Software
9 Sept 202419:49

TLDRAt CPX 2024, the development journey of an AI pilot was highlighted, emphasizing the complexity beyond integrating a large language model like CH GPT into applications. The talk covered the rapid adoption of AI in enterprise, the unique engineering challenges faced, and the importance of security with AI. It showcased the AI co-pilot's capabilities, including handling network and security tasks, and future research directions like multimodality, collaboration with external systems, and integration into chat workflows.

Takeaways

  • 😀 The speaker is excited to discuss the development of an AI pilot at Check Point, emphasizing the uniqueness and complexity of the project.
  • 🚀 CH GPT, launched in November 2022, quickly reached 100 million monthly active users, marking it as the fastest-growing consumer application in history.
  • 🌐 The rapid adoption of generative AI in enterprise business has led to a surge in companies developing their own AI-powered applications.
  • 🛡️ In cybersecurity, hackers have begun using AI to develop tools like spam email generators and methods to bypass KYC (Know Your Customer) protocols.
  • 🤖 Check Point is working on two AI projects: a virtual assistant for network administrators and one for security analysts, which were recently announced.
  • 🧠 The AI model, referred to as the 'brain' of the co-pilot, is compared to children, highlighting the similarities and differences among various AI models.
  • 🔒 An 'AI Firewall' component is introduced to ensure the AI behaves securely and safely, preventing it from performing unauthorized actions or providing harmful information.
  • 🛠️ The speaker discusses the use of 'few-shot prompting' and 'Chain of Thought prompting' as tools for teaching the AI specific skills and handling complex tasks.
  • 📈 A novel method for automatically generating AI-friendly descriptions of functions and APIs is being patented by Check Point to enhance the AI's capabilities.
  • 🔍 The AI copilot is designed to work within customer environments, minimizing data exposure and focusing on security and safety.
  • 🔮 Check Point is exploring future AI capabilities, including multimodality, collaboration with external systems like JIRA, and integration into common chat platforms.

Q & A

  • What was the main topic of the speech at CPX 2024?

    -The main topic of the speech at CPX 2024 was the development and implementation of AI, specifically Large Language Models (LLMs), in the context of Check Point's security solutions.

  • How was the audience's feedback described at the event?

    -The audience's feedback was described as overwhelming, indicating a highly positive and engaged response to the presentations and demonstrations at the event.

  • What was the significance of the AI pilot project discussed?

    -The AI pilot project was significant because it represented a unique and complex engineering challenge, involving the integration of generative AI into Check Point's applications, which is more intricate than simply embedding an AI widget.

  • What was the growth rate of CH GPT mentioned in the speech?

    -CH GPT was mentioned to have reached 100 million monthly active users just two months after its launch, making it the fastest-growing consumer application in history.

  • How have hackers been using AI technology according to the speech?

    -Hackers have been using AI technology to accelerate the development of simple tools and malware, and more recently, to create proven and tested applications to bypass security measures like spam filters and account verification.

  • What were the two projects that Check Point was working on, as mentioned in the speech?

    -Check Point was working on two projects: a virtual assistant for network administrators and a virtual assistant for security analysts.

  • What was the unique aspect of engineering with generative AI compared to traditional programming?

    -The unique aspect of engineering with generative AI is that it involves guiding, explaining, and teaching the AI model, rather than simply coding instructions for it to follow.

  • What is the role of the 'LLM Manager' in Check Point's AI architecture?

    -The 'LLM Manager' in Check Point's AI architecture is responsible for selecting the best model for each type of question or instruction, ensuring the most appropriate AI is used for the task at hand.

  • Why is the 'AI Firewall' component necessary in Check Point's system?

    -The 'AI Firewall' component is necessary to ensure that the AI does not perform actions it shouldn't, such as executing API commands based on misinformation, generating harmful speech, or providing unclear or unsafe answers.

  • What are 'few shots prompting' and 'Chain of Thought prompting' in the context of AI?

    -Few shots prompting is a technique where examples of questions and answers are provided to guide the AI's responses. Chain of Thought prompting goes further by also explaining the reasoning process needed to reach the answer.

  • What is the significance of the patent-pending method mentioned in the speech?

    -The patent-pending method is significant because it allows for the automatic generation of LLM-friendly descriptions of functions and APIs, which can be dynamically integrated into prompts to guide the AI's actions.

Outlines

00:00

🌟 Introduction to AI and Generative AI in Business

The speaker begins by expressing excitement over the audience's response to the Expo and acknowledges the rapid growth of CH GPT, which gained 100 million monthly active users within two months of its launch. The speaker emphasizes the unique engineering challenge of integrating AI into applications, noting the complexity of building an AI pilot. The talk also touches on the broader implications of AI, such as its adoption in enterprise business and cybersecurity, where hackers are now using generative AI to bypass security measures. The speaker concludes this section by reflecting on two ongoing projects: a virtual assistant for network administrators and a virtual assistant for security analysts, which were announced the previous day.

05:01

🤖 The Unique Nature of Developing AI Applications

The speaker delves into the challenges and unique aspects of developing AI applications, comparing the process to raising a child rather than traditional programming. They discuss the variability among different AI models, highlighting how each has its strengths and weaknesses in areas like language comprehension, reasoning, and cost. The speaker uses an example of a math problem to illustrate the differences in performance between Bard and CH GPT. They also introduce the concept of the 'LLM Manager' within their AI architecture, which selects the most appropriate AI model for a given task. The paragraph concludes with a discussion on the importance of security and safety in AI, introducing the 'AI Firewall' component designed to prevent the AI from performing unauthorized actions or providing harmful content.

10:02

🔒 Ensuring Security and Teaching AI New Skills

This section focuses on the security measures implemented to safeguard the AI system against prompt injections and other potential threats. The speaker uses the analogy of tricking a child to eat vegetables to explain how easy it is to manipulate AI without sophisticated hacking skills. They describe the 'AI Firewall' component that ensures the AI adheres to safety protocols. The speaker then explains the process of teaching AI new skills through 'few-shot prompting' and 'Chain of Thought prompting,' which involve providing examples and explaining the thought process required to reach an answer. The paragraph provides examples of how these techniques are applied in practice, such as generating API commands and querying log servers.

15:02

🚀 Future Directions: Multimodal AI and Integration with Other Systems

The speaker outlines ongoing research projects that aim to expand the capabilities of AI copilots. They discuss the potential of multimodal AI, which can understand and process different types of data beyond text, such as images and sound. An experiment is described where an AI model successfully interpreted a hand-drawn network topology and provided accurate firewall rule recommendations. The speaker also touches on the research into enabling AI copilots to collaborate with other systems, such as Jira, to fetch and resolve tickets autonomously. Lastly, they explore the idea of integrating AI copilots into common chat applications like WhatsApp or Slack, envisioning a future where users can interact with AI assistants through their preferred communication platforms.

Mindmap

Keywords

💡AI Pilot

The term 'AI Pilot' refers to an advanced pilot program or project that incorporates artificial intelligence to perform tasks or make decisions. In the context of the video, it represents the development of a system that is not just a simple integration of AI into an application, but a complex project that requires careful guidance and teaching, much like raising a child. The AI Pilot is designed to assist in specific roles, such as a virtual assistant for network administrators and security analysts, showcasing the practical application of AI in real-world scenarios.

💡LLM (Large Language Models)

LLM stands for Large Language Models, which are AI models trained on vast amounts of text data to understand and generate human-like text. They are a core component of the AI Pilot discussed in the video. The script mentions how these models are selected and managed within the AI system to handle different types of queries effectively. The video also highlights the uniqueness and capabilities of LLMs, such as their ability to comprehend natural language and provide reasoned answers.

💡Generative AI

Generative AI refers to AI systems that can create new content, such as text, images, or music, based on the data they have been trained on. In the video, generative AI is mentioned in the context of its rapid integration into various applications and the potential risks associated with its misuse, such as generating spam emails or falsifying identification documents. It underscores the dual-use nature of AI technology, highlighting both its benefits and the challenges it poses.

💡Cybersecurity

Cybersecurity is the practice of protecting systems, networks, and data from digital attacks. In the video, cybersecurity is a significant theme, with discussions on how AI, particularly generative AI, is being used both to enhance security measures and to facilitate cyberattacks. The video also touches on the development of AI tools to assist security analysts, indicating the evolving role of AI in this field.

💡Virtual Assistant

A 'Virtual Assistant' in the video refers to an AI-driven tool designed to assist users in performing tasks or providing information. The script describes the development of virtual assistants for network administrators and security analysts, emphasizing the unique engineering challenges in creating these AI systems. These assistants are meant to streamline workflows and provide expert-level support, showcasing the integration of AI into professional roles.

💡Prompt Injection

Prompt Injection is a technique where an input is manipulated to trick an AI system into performing an unintended action. The video discusses the naivety of LLMs to such injections, illustrating how simple prompts can sometimes bypass the AI's safeguards. This concept is crucial for understanding the security measures needed when developing AI systems to ensure they operate safely and as intended.

💡AI Firewall

The 'AI Firewall' mentioned in the video is a component of the AI system designed to ensure that the AI behaves securely and safely. It acts as a guardrail to prevent the AI from performing actions it shouldn't, such as executing harmful commands or providing unclear or harmful advice. This concept is integral to the discussion on maintaining the integrity and safety of AI applications in various environments.

💡Few-Shot Prompting

Few-Shot Prompting is a technique in AI where the model is provided with a few examples of the desired output along with the input. This helps the model understand the task better and generate more accurate responses. In the video, this technique is discussed as part of the process of teaching the AI Pilot skills, emphasizing the importance of providing context and examples to guide the AI's learning process.

💡Chain of Thought Prompting

Chain of Thought Prompting is a method where the AI is not only given examples but also guided through the logical steps needed to arrive at the answer. This is used in more complex tasks where the AI needs to understand the sequence of actions or reasoning required to complete a task. The video uses this concept to explain how the AI Pilot is taught to perform multi-step operations, such as querying a log server for security events.

💡LLM Manager

The 'LLM Manager' is a component of the AI system described in the video, responsible for selecting the most appropriate LLM for a given task or query. This manager ensures that the system uses the best model that fits the specific requirements of the task at hand, adapting to the evolving capabilities of different LLMs. The concept highlights the complexity of managing AI systems and the need for dynamic selection of AI models to optimize performance.

Highlights

Excitement over the audience's feedback on Check Point's AI pilot.

Building an AI pilot is more complex than simply integrating a large language model like GPT.

GPT-3 reached 100 million monthly active users two months after its launch, setting a record.

The rapid growth of generative AI in enterprise business use post-GPT-3 launch.

Cybersecurity implications of AI, including hackers using it to develop malware and bypass security measures.

Introduction of Check Point's AI projects: a virtual assistant for network administrators and a virtual assistant for security analysts.

The unique engineering challenge of guiding AI models rather than programming them.

Comparison of different large language models (LLMs) on metrics like prompt size, latency, and reasoning ability.

The LLM manager component that selects the best model for a given question or instruction.

Addressing security and safety concerns with the AI firewall component.

Using 'few shots prompting' and 'chain of thought prompting' to teach the AI new skills.

Automatic generation of LLM-friendly descriptions of functions and APIs, a patent-pending innovation.

Teaching the AI to query log servers and understand different types of logs.

The AI copilot's ability to understand network topology drawings and suggest firewall rules.

Research into enabling the AI copilot to collaborate with external systems like Jira.

Exploring the integration of the AI copilot into chat-based workflows for 24/7 virtual assistance.

Anticipation for the AI copilot's real-world application and its potential impact.

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