A-DSRM Suite
A-DSRM Suite is an open-source toolchain implementing the Agile DSRM methodology for adversarial AI research and development. The suite comprises four integrated components: D²EFR Manager, Validity Monitor, Policy Transformer, and Experiment Orchestrator.
IEEE Access (Special Section on AI Security)
Impact Factor: Fast-track, high visibility • Timeline: 3-4 months
DSRM Lifecycle Coverage
Artifact Overview
Methodological innovation requires practical tooling to achieve widespread adoption. Ad-hoc approaches to adversarial AI research reduce reproducibility, increase vulnerability windows, and prevent systematic knowledge accumulation.
Practitioners, tool developers, researchers, and teams building security artifacts who need structured methodology support.
CaseStudy4 metrics
Key Contributions
First comprehensive toolchain for DSRM in adversarial AI
AdvAI-Bench dataset with diverse attack profiles
Large-scale usability study (100 projects)
Paper Structure
Section 1
The Tooling Gap
Section 2
A-DSRM Suite Architecture
Section 3
Component Specifications
Section 4
AdvAI-Bench Dataset
Section 5
Effectiveness & Usability Results
Section 6
Integration Case Studies
1. Problem Statement & Operational Motivation
Methodological innovation requires practical tooling to achieve widespread adoption. Ad-hoc approaches to adversarial AI research reduce reproducibility, increase vulnerability windows, and prevent systematic knowledge accumulation.
This problem arises in the context of practitioners, tool developers, researchers, and teams building security artifacts who need structured methodology support. and reflects constraints commonly encountered in production systems, including scale, adversarial behavior, regulatory requirements, and operational continuity.
2. Artifact Description
A-DSRM Suite is an open-source toolchain implementing the Agile DSRM methodology for adversarial AI research and development. The suite comprises four integrated components: D²EFR Manager, Validity Monitor, Policy Transformer, and Experiment Orchestrator.
The artifact is designed to be identity-first, treating authentication, authorization, federation, and policy enforcement as the primary control plane. It is intended to function under real operational conditions rather than idealized assumptions.
3. Design Science Research Methodology (DSRM) Mapping
A-DSRM Suite follows DSRM with research contributions expressed as an operational artifact.
• Problem Identification & Motivation
The operational problem was defined based on observed risks and limitations in existing systems.
• Design & Development
A-DSRM Suite is built on the following design principles:
- Methodology-first tooling design
- Reproducibility as a first-class requirement
- Integration with existing CI/CD pipelines
- Open-source community sustainability
• Build
The suite comprises: (1) D²EFR Manager for Define-Design-Evaluate-Refine workflow orchestration, (2) Validity Monitor for continuous drift detection and alerting, (3) Policy Transformer for NIST-to-IAM policy translation, and (4) Experiment Orchestrator for reproducible adversarial testing. Released with Docker containers, Kubernetes operators, and VSCode extensions.
• Demonstration
Benchmark evaluation across 100 research projects with AdvAI-Bench dataset containing 50+ attack profiles.
• Evaluation
Teams using A-DSRM Suite reduced vulnerability window exposure by 73%, improved documentation completeness by 89%, and increased research reproducibility scores from 45% to 92% compared to ad-hoc methodologies.
• Communication
The artifact is documented as a citable protocol object and connected to research notes, simulation plans, and deployment guidance.
4. Evaluation & Evidence
Evaluation Method: CaseStudy
Evaluation Metrics:
- Vulnerability window exposure reduction (73%)
- Documentation completeness improvement (89%)
- Research reproducibility score improvement (45% → 92%)
- Integration time for existing projects
Evaluation Contexts:
- Performance evaluation across 100 research projects
- AdvAI-Bench dataset with 50+ attack profiles
- CI/CD integration case studies
- Usability study with research teams
The evaluation approach treats the environment as adversarial and constrained. A-DSRM Suite is not assessed on theoretical correctness alone; it is assessed on whether it can deliver trustworthy behavior under realistic deployment assumptions.
5. Key Citations & Foundations
- •Kitchenham et al. (2009) - Systematic review guidelines
- •Pasquier et al. (2017) - Provenance in cybersecurity
- •Hütten et al. (2021) - AI security benchmarks
6. Applicability & Use Cases
A-DSRM Suite applies to:
Use cases include:
- Architecture design and review
- Security control implementation
- Research extension and replication
- Teaching and laboratory exercises
- Policy and governance analysis
7. Limitations & Scope
Learning curve for teams unfamiliar with DSRM. Some features require Kubernetes infrastructure. AdvAI-Bench coverage expanding but not exhaustive.
8. Iteration & Evolution
Toolchain evolves based on community feedback, new attack profiles in AdvAI-Bench, and integration requests for additional CI/CD platforms.
9. How to Cite This Artifact
J. Nsoh, "A-DSRM Toolchain: Open-Suite Implementation for Adversarial AI Research and Development," IEEE Access, 2025. Available: https://jovita.io/artifacts/a-dsrm-toolchain
10. Related Research & Teaching
11. License & Availability
License: Apache 2.0
Last Updated: 2025-11-10
Implementation Links:
Where applicable, reference implementations and simulation configurations will be published as linked materials under this artifact record.
A-DSRM Suite represents an applied research contribution produced through Design Science Research Methodology. Its value lies not only in correctness, but in whether it can be implemented, evaluated, and trusted in real operational environments.