Enterprise AI Analysis
Data Security Storage and Transmission Framework for AI Computing Power Platforms
Authors: Jiefei Chen, Zhiliang Lu, Hua Zheng, Zhengguo Ren, Yuanfeng Chen & Jianing Shang
DOI: 10.1038/s41598-025-31786-5
In the era of rapidly expanding artificial intelligence (AI) applications, ensuring secure data storage and transmission within AI computing power platforms remains a critical challenge. This research presents a novel data security storage and transmission system, termed as secure artificial intelligence data storage and transmission (Secure AI-DST), tailored for AI computing environments. The proposed framework integrates a hybrid encryption mechanism that combines Amended Merkle Tree (AMerT) hashing with Secret Elliptic Curve Cryptography (SEllC) enhanced data confidentiality. For secure storage and decentralization, the system leverages blockchain with InterPlanetary File System (IPFS) integration, ensuring tamper-proof and scalable data handling. To classify various attack types, a novel deep learning model attention bidirectional gated recurrent unit-assisted residual network (Att-BGR) is deployed, offering accurate detection of intrusions. Simulation studies conducted in MATLAB® 2023b using both synthetic and real-time datasets show that the Secure AI-DST system reduces unauthorized access attempts by 92.7%, maintains data integrity with 99.98% accuracy under simulated cyberattacks, and achieves a packet validation success rate of 97.6% across edge-to-cloud transmissions. Furthermore, the proposed method introduces only a 4.3% computational overhead, making it highly suitable for real-time AI workloads. These outcomes confirm the effectiveness of Secure AI-DST in ensuring end-to-end data guard, resilience against cyber threats, and scalable presentation for next-generation AI computing substructures.
Executive Impact & Key Performance Indicators
The Secure AI-DST framework delivers robust security and efficiency, critical for next-generation AI operations.
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Unauthorized Access Reduction
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Data Integrity Accuracy
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Packet Validation Success Rate
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Minimal Computational Overhead
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
AES-256 Encryption
SHA-3 Hashing
SMPC
Blockchain Auth
Att-BGR Model
Limitations & Future
AES-256 Encryption for Confidentiality
AES-256 is a symmetric block cipher that processes data in blocks of 128 bits using a 256-bit key. The encryption involves 14 rounds of substitution, permutation, and mixing processes governed by the Rijndael algorithm. This ensures strong resistance against brute-force and cryptanalysis attacks, vital for protecting large-scale AI data during storage and transmission.
SHA-3 For Data Integrity Verification
To ensure data integrity, SHA-3 hashing is utilized, grounded on the Keccak sponge construction. It takes an input of random length and generates a fixed-length hash (e.g., 256 or 512 bits). The collision resistance of SHA-3 ensures that any modification to data will result in a drastically dissimilar hash, providing a robust mechanism for detecting real-time tampering in AI communication systems.
Secure Multi-Party Computation (SMPC)
SMPC enables collaborative computation across multiple parties while keeping individual inputs private. Using Shamir's Secret Sharing, a secret is divided into shares among parties. This method ensures both privacy and fault tolerance, allowing decentralized AI inference across cloud-edge nodes without exposing raw data.
Blockchain-Enabled Decentralized Authentication
To decentralize trust and ensure tamper-proof storage, the framework leverages blockchain. Each block contains a timestamp, hash of the previous block, and current data hash. This forms an immutable ledger, eliminating single points of failure. Smart contracts enforce data access policies, and off-chain storage via IPFS minimizes costs.
Noval Att-BGR Model for Attack Detection
A novel deep learning model, Attention Bidirectional Gated Recurrent Unit-Assisted Residual Network (Att-BGR), is introduced to detect, classify, and localize various attack types. This hybrid model fuses Bi-GRUs with attention mechanisms and residual learning, achieving 98.38% classification accuracy and maintaining robustness against unseen attack profiles.
Limitations & Future Recommendations
Current limitations include evaluation in a controlled testbed (not fully real-world), limited testing on resource-constrained edge devices, and lack of quantum-resistance mechanisms. Future work will integrate post-quantum cryptographic (PQC) primitives, federated learning, and zero-trust security architectures for enhanced resilience and real-world pilot deployments.
Enterprise Process Flow: Secure AI-DST Implementation Strategy
1. Data Acquisition
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2. Data Preprocessing
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3. Hashing via Amended Merkle Tree (AMerT)
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4. Encryption using AES-256
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5. Integrity Validation using SHA-3
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6. Blockchain-Based Storage via IPFS
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7. Secure Multi-Party Computation (SMPC)
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8. Intrusion and Attack Detection Using Att-BGR Model
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9. Decryption at Authorized Endpoints
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10. Performance Evaluation and Feedback Loop
98.38%
AI Attack Classification Accuracy (Att-BGR Model)
Comparative Performance Evaluation with Recent Security Frameworks
| Framework | Integrity Verification (%) | Latency (ms) | Overhead (%) | Detection Accuracy (%) |
|---|---|---|---|---|
| PQC-based Security Model | 96.8 | 58.3 | 13.2 | 94.7 |
| FL-based IDS Framework | 97.5 | 52.6 | 11.5 | 95.3 |
| Lightweight Blockchain Model | 95.9 | 49.7 | 9.6 | 93.1 |
| Proposed Framework | 99.2 | 41.5 | 8.7 | 98.4 |
Real-World Enterprise Application Scenario: AI Computing Platforms
The Secure AI-DST framework is purpose-built for enterprise AI computing platforms, ensuring robust data security across distributed environments—from edge devices to cloud infrastructure. Its integrated approach, combining AES-256 encryption, SHA-3 hashing, blockchain-IPFS storage, and Att-BGR for intrusion detection, provides unparalleled confidentiality and integrity for sensitive AI data.
This system directly addresses critical enterprise needs:
- Secure Data Processing: Protects proprietary AI models and training datasets from unauthorized access and tampering.
- Compliance & Trust: Immutable blockchain records ensure auditability and regulatory compliance for data handling.
- Efficient Operations: Low computational overhead (4.3%) and optimized latency (<225ms) support real-time AI workloads without compromising performance.
- Cyber Resilience: The Att-BGR model's high detection accuracy (98.38%) safeguards against diverse cyber threats, enhancing system reliability.
By implementing Secure AI-DST, enterprises can confidently scale their AI initiatives, knowing their data assets are protected against evolving cyber threats, ensuring operational continuity and competitive advantage.
Calculate Your Potential AI Security ROI
Estimate the financial and operational benefits of implementing advanced AI security solutions within your enterprise.
Estimated Annual Cost Savings
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Annual Hours Reclaimed
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Your Implementation Roadmap to Secure AI
A phased approach to integrate the Secure AI-DST framework into your existing enterprise architecture.
Phase 01: Assessment & Strategy (2-4 Weeks)
Comprehensive analysis of existing AI infrastructure, data flow, and security vulnerabilities. Define key requirements and tailor the Secure AI-DST components (encryption, hashing, blockchain, Att-BGR) to your specific enterprise needs. Establish project milestones and success metrics.
Phase 02: Pilot Deployment & Integration (6-10 Weeks)
Deploy a sandbox instance of the Secure AI-DST framework. Integrate core cryptographic layers (AES-256, SHA-3) and blockchain-IPFS storage with a non-critical AI workload. Conduct initial performance and security tests on a subset of data. Gather feedback and refine configurations.
Phase 03: Att-BGR Model Training & Tuning (4-8 Weeks)
Train the Att-BGR deep learning model on your enterprise-specific data traffic and known attack patterns. Fine-tune model parameters for optimal intrusion detection accuracy and minimal false positives. Validate model performance against simulated real-world cyber threats.
Phase 04: Full-Scale Rollout & Monitoring (8-16 Weeks)
Gradual deployment of the Secure AI-DST framework across all critical AI computing platforms and data pipelines. Implement continuous monitoring, automated threat response, and regular security audits. Establish a feedback loop for ongoing optimization and adaptation to new threats.
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