Automated Security Assurance with Itcan

Introduction

• The increasing complexity of modern software systems has elevated the risk of security issues.
• Current methods for verifying security compliance are often manual or semi-automated.
• There's a critical need for automated tools to verify code standards.
• The proposed tool, "Itcan," uses LLMs for automated security assurance.

Problem Statement

• Manual code review is inefficient and prone to human error.
• Existing tools often fail to integrate with broader security standards.
• Current methods struggle to ensure consistent application of security standards.

Suggested Solution

• The proposed system, "Itcan," integrates LLMs with established security standards and a rule-based engine.
• "Itcan" aims to verify code compliance with stringent security standards across multiple programming languages.
• The system processes code, generates reports, and provides actionable recommendations for remediation.

Research Aim and Objectives

• To develop an automated system using LLMs to ensure compliance with cybersecurity standards.
• To evaluate how effectively LLMs translate standards into practical rules for verification.
• To assess the precision and recall metrics of the system in minimizing false positives and negatives.

Report Outline

• Chapter 2: Background on secure coding and LLMs.
• Chapter 3: Literature review on related research.
• Chapter 4: System design details.
• Chapter 5: Evaluation methodology and materials list.
• Chapter 6: Summary and future directions.
• The implementation and results will be discussed in a later phase (GP2).

Background

• Secure Coding Practices: Guidelines and techniques to minimize software security risks.
• Automated Security Standard Enforcement: Using tools to ensure code adheres to standards.
• Machine Learning (ML): Enables systems to learn from data to detect patterns in code adherence.
• Large Language Models (LLMs): Advanced deep learning models for natural language processing, useful in code analysis.

Literature Review

• Overview of recent studies using AI techniques for software security analysis.
• Detailed description of relevant studies from various journals and conferences.

System Design

• Diagram showing the input (source code, standards, prompts), LLMs, rule-engine and output.
• Itcan takes code as input.
• It integrates standards from organizations and established practices to create a baseline.
• LLM analyses code segments.
• A rule-based engine assesses code against standards and generates reports.

Evaluation Methodology

• Research questions regarding LLM efficiency in security standard translation to actionable rules, precision/recall metrics, and developer feedback.
• Hardware details (MacBook Pro, M1 chip, macOS, 8 core CPU, 8 GB RAM).
• Software requirements (Open-source LLMs, Semgrep).
• Procedures for data collection, preprocessing, prompt engineering, and system evaluation.

Results and Discussions

• Detailed results and analysis of the LLM-based system will be presented and discussed in the subsequent semester.

Conclusion

• The full conclusion will be deferred to the next phase.

Explore the innovative tool 'Itcan' that leverages Large Language Models (LLMs) for automated security assurance in software code. This quiz delves into the complexities of software security, the inefficiencies of manual reviews, and how 'Itcan' improves compliance with security standards across multiple programming languages.