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Enterprise AI Analysis: A generative AI cybersecurity risks mitigation model for code generation: using ANN-ISM hybrid approach

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

0% Avg. Risk Reduction
0% Model Prediction Accuracy
0 Mitigation Maturity

Deep Analysis & Enterprise Applications

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Abstract & Context
Methodology Flow
Key Findings
Case Study Evaluation

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

Phase 1: Multivocal Literature Review
Phase 2: Online Questionnaire Survey
Phase 3: Expert Panel Review
Phase 4: Artificial Neural Network (ANN)
Phase 5: Interpretive Structural Modeling (ISM)
Phase 6: Develop Hybrid ANN-ISM Framework
94% MLR & Real-World Study Correlation

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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Estimated Annual Savings $0
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Implementation Roadmap

Our structured approach ensures a seamless integration of AI into your enterprise, maximizing impact and minimizing disruption.

Phase 1: Discovery & Strategy

In-depth assessment of current workflows, identification of AI opportunities, and development of a tailored implementation strategy.

Phase 2: Pilot & Validation

Deployment of AI solutions in a controlled environment, rigorous testing, and validation of initial results against KPIs.

Phase 3: Scaled Deployment

Full-scale integration across relevant departments, comprehensive training, and continuous monitoring for optimal performance.

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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