AI-blockchain integration can strengthen threat detection and auditability

The race to strengthen cybersecurity has entered a new phase as researchers have unveiled a novel system that combines artificial intelligence with blockchain to improve real-time cyber defense. Their study, titled AI-Blockchain Integration for Real-Time Cybersecurity: System Design and Evaluation and published in the Journal of Cybersecurity and Privacy, details a prototype capable of detecting attacks with high precision while ensuring immutable logging for audit and compliance.

The work highlights a path forward for enterprises and regulated industries under mounting pressure from increasingly sophisticated cyberattacks. By joining anomaly detection with transparent, tamper-resistant records, the system targets one of the field’s most persistent gaps: trust in monitoring, response, and forensic evidence.

Why AI alone is not enough for cybersecurity

The researchers frame their work around a fundamental question: why has artificial intelligence, despite its strong performance in anomaly detection, not delivered complete cybersecurity assurance? The answer lies in the limits of AI models when used in isolation.

Anomaly detection algorithms, even when highly accurate, typically operate as black boxes, raising concerns about explainability, trustworthiness, and accountability. False positives or adversarial manipulation can undermine their reliability. Moreover, once alerts are generated, organizations often lack a transparent and verifiable chain of evidence to show when and how models flagged a potential threat.

The authors argue that this lack of trust and auditability leaves a blind spot in cybersecurity operations. By integrating blockchain, they introduce immutability and provenance into the process. Every AI-generated alert, along with metadata such as confidence scores and model versions, is logged into a permissioned blockchain. This ensures forensic integrity, allowing regulators, auditors, and security teams to verify both the detection process and its outcomes.

How the prototype works in real time

The second critical question the study addresses is how to achieve integration between machine learning-based detection and blockchain without undermining performance. The authors developed a layered system with two main components: a convolutional neural network for real-time anomaly detection and a permissioned Ethereum-based blockchain for logging results.

The AI component was tested against alternative models such as LSTM and GRU. After extensive evaluation on the CICIDS2017 intrusion detection dataset, the convolutional neural network emerged as the strongest performer. It delivered a precision improvement from 85.2 percent in baseline models to 93.4 percent, with corresponding gains in recall and overall detection accuracy. The model was deployed via a lightweight Flask REST API, enabling seamless integration into operational systems.

On the blockchain side, the system relied on smart contracts written in Solidity to record model metadata, detection outcomes, and timestamps. Unlike public blockchains, which suffer from scalability and privacy issues, the system uses a permissioned approach. This ensures whitelisted access, predictable performance, and greater suitability for enterprise deployment. To further reduce latency, the authors incorporated layer-two scaling strategies, making the system capable of near real-time logging.

Performance testing confirmed that the combined AI-blockchain prototype was viable under operational loads. It sustained approximately 88 transactions per second with average latency near 60 milliseconds. Even under stress, latency stayed within a 28–210 millisecond range, suitable for practical deployment in live environments.

What the findings mean for the future of cybersecurity

The study also assesses the impact AI-blockchain integration could have on the future of cybersecurity. The findings suggest that the prototype bridges a crucial gap between detection and trust.

For industries such as finance, healthcare, and government, regulatory compliance increasingly demands audit trails that cannot be tampered with. By embedding blockchain into the anomaly detection pipeline, organizations gain both real-time defense and immutable evidence for investigations and compliance reporting. This approach could redefine standards for forensic readiness, shifting from traditional log management systems to verifiable, cryptographically secured records.

However, the study also acknowledges challenges. Blockchain integration, while valuable for transparency, adds overhead compared with AI-only pipelines. Though the prototype minimized latency, scaling to higher transaction volumes or more complex models may require further innovation. Privacy concerns also remain. Even in permissioned chains, sensitive threat data needs cryptographic protection, with methods such as zero-knowledge proofs or homomorphic encryption offering potential solutions.

The authors highlight future directions including broader adoption of sidechains, expanded lifecycle logging to include model training and retraining steps, and privacy-preserving mechanisms to meet evolving regulatory frameworks. These enhancements could make the system adaptable across industries and jurisdictions.