Enterprise AI Analysis
A generative AI cybersecurity risks mitigation model for code generation: using ANN-ISM hybrid approach
Leveraging ANN-ISM Hybrid Approach for Enhanced Security in Code Generation.
Executive Impact
Deep Analysis & Enterprise Applications
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This paper introduces a novel Hybrid Artificial Neural Network (ANN)-Interpretive Structural Modeling (ISM) Framework to mitigate cybersecurity risks in automatic code generation using Generative AI. The framework integrates ANN's predictive capabilities with ISM's structured analysis for identifying, evaluating, and treating common vulnerabilities.
Research Flow Framework
Our comprehensive six-phase approach ensures robust verification and validation of the proposed hybrid ANN-ISM framework for reducing cybersecurity risks in automated code generation.
The case study results demonstrate the framework's efficiency in handling primary cybersecurity challenges such as injection attacks, code quality, backdoors, and input validation. Advanced risk mitigation is enabled across multiple process areas, with techniques like static code analysis and adversarial training showing promise.
AI Code Generation Company Implementation
A medium-sized firm using Generative AI for software development evaluated the Hybrid ANN-ISM framework. The company showed Advanced (3) mitigation in critical processes like code quality, backdoors, and input validation.
Strengths Identified:
- Strong static code analysis, passive penetration testing, and fuzz testing.
- Successful leverage of AI-based utilities for security audits.
- Automated secure logging and real-time monitoring at advanced maturity.
Challenges & Areas for Improvement:
- Adversarial attacks on AI models still at Development (2) level.
- Over-reliance on AI models needs further mitigation.
- Privacy and data leakage at Comprehension (1) level, requiring enhancement.
Enterprise Process Flow
AI Code Generation Company Implementation
A medium-sized firm using Generative AI for software development evaluated the Hybrid ANN-ISM framework. The company showed Advanced (3) mitigation in critical processes like code quality, backdoors, and input validation.
Strengths Identified:
- Strong static code analysis, passive penetration testing, and fuzz testing.
- Successful leverage of AI-based utilities for security audits.
- Automated secure logging and real-time monitoring at advanced maturity.
Challenges & Areas for Improvement:
- Adversarial attacks on AI models still at Development (2) level.
- Over-reliance on AI models needs further mitigation.
- Privacy and data leakage at Comprehension (1) level, requiring enhancement.
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Implementation Roadmap
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Phase 2: Pilot & Validation
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Phase 3: Scaled Deployment
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Phase 4: Optimization & Future-Proofing
Ongoing refinement, adaptation to evolving business needs, and exploration of advanced AI capabilities for sustained competitive advantage.
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