Bridging Domains: Advances in Explainable, Automated, and Privacy-Preserving AI for Computer Science and Cybersecurity

Artificial intelligence (AI) is rapidly redefining both computer science and cybersecurity by enabling more intelligent, scalable, and privacy-conscious systems. While most prior surveys treat these fields in isolation, this paper provides a unified review of 256 peer-reviewed publications to bridge that gap. We examine how emerging AI paradigms, such as explainable AI (XAI), AI-augmented software development, and federated learning, are shaping technological progress across both domains. In computer science, AI…

By the Truvace deskOctober 7, 20261 min read1 sourcePeer-reviewedTRV-2026-1307· cite & verify

In brief

On 2025-09-08, a peer-reviewed review in Computers synthesized 256 publications to bridge computer science and cybersecurity advances in explainable AI, automated development, and privacy-preserving learning. It reports AI embedded across the software lifecycle for productivity and testing, and AI-driven real-time threat detection in security operations.

The breach-reduction finding matters because it quantifies a direct crime-prevention benefit, but the same synthesis flags unresolved fairness, adversarial robustness, interpretability gaps, and high computational costs that could limit trustworthy deployment in decentralized IoT and healthcare settings.

Main points

Unified review of 256 peer-reviewed publications bridging computer science and cybersecurity.
In computer science, AI embedded throughout software development lifecycle to boost productivity and automate testing and decision making.
Privacy-preserving techniques including federated learning and local differential privacy identified as essential safeguards in IoT and healthcare.

The gain

Peer-reviewed synthesis finds AI-enabled cybersecurity defenses reduce successful breaches by up to 30% in real-world deployment contexts.

The rundown

The authors conducted a unified review of 256 peer-reviewed publications to examine explainable AI, AI-augmented software development, and federated learning across computer science and cybersecurity.

In cybersecurity, the synthesis reports AI drives real-time threat detection and adaptive defense, with empirical evidence of up to 30% reduction in successful breaches, while explainability is framed as cornerstone for trust and bias mitigation.