A notable concern is that AI systems themselves must be protected—covering training data, pipelines, storage, networks, and infrastructure—with controls such as access management, encryption, data validation, and secure data handling.

AI techniques like machine learning, deep learning, and natural-language processing support network-intrusion detection, traffic analysis, malware and phishing detection, fraud analysis, incident response, and vulnerability management, with adaptive models aiding in unseen attacks and automation speeding responses.

Economic effects are mixed: automation and improved threat detection can trim costs, but high initial investments and the need for skilled staff pose barriers for smaller organizations and resource-constrained countries, with no independent forecast or net-benefit calculation provided.

About 80 nations have national cybersecurity strategies tailored to their contexts, yet policy differences hinder international coordination.

A comprehensive review examines AI for cybersecurity and cybersecurity for AI, mapping national strategies, economic implications, and policy recommendations across the United States, European Union, China, UAE, Africa, and Ethiopia using a PRISMA 2020-guided approach and multiple databases.

Limitations of the review include English-language bias, exclusion of conference proceedings, a February 2025 cutoff, qualitative heterogeneity, and no formal meta-analysis, yielding a broad synthesis rather than head-to-head effectiveness comparisons.

Key recommendations call for privacy-by-design, bias audits, explainable AI, human oversight, workforce education, public-private partnerships, cross-border policy coordination, and proactive planning for emerging threats, with future work on quantum-resistant cybersecurity, lightweight solutions for resource-limited settings, standardized benchmarks, and greater representation of underrepresented regions.

Literature classifies adversarial attacks by model access (white-box, black-box, gray-box) and documents risks such as data poisoning, AI-enabled phishing and malware, synthetic media, false positives, biased or incomplete data, privacy concerns, and model opacity.

National strategies differ: the US, China, and the EU lead prominent AI initiatives; the EU emphasizes rights and regulatory compliance, China prioritizes national security and public safety, and the UAE pursues its AI Strategy 2031, reflecting that over 40 regions have elevated AI to national-strategy status since 2016.

Implementation is the dominant theme, with studies favoring adaptive, forward-looking defenses over static controls, while highlighting widespread shortages of AI and cybersecurity expertise.

Africa faces constraints including limited technical expertise, budget limits, data quality issues, infrastructure gaps, weak laws, and implementation capacity; Ethiopia is highlighted through its Digital Ethiopia 2030 strategy but lacks a specific AI-cybersecurity plan and faces concerns over critical infrastructure cyberattacks, data protection, sovereignty, and institutional capacity.

A systematic review of 53 studies (2016–February 2025) finds AI can enhance threat detection, automated response, and vulnerability management, while also introducing security and ethical risks.