Introduction

As digital transformation continues to accelerate, artificial intelligence is becoming increasingly embedded in organizational workflows. While this development offers significant opportunities for efficiency and innovation, it also contributes to a more complex and evolving cyber threat landscape. AI technologies are used not only as defensive tools, but also by malicious actors to develop attack vectors that are faster, more scalable, and highly targeted. This dual use extends cybersecurity beyond technical safeguards alone, making it necessary to adopt a comprehensive security approach integrated with organizational governance, risk management, and data protection strategies. In this article, we examine the emerging cyber risk dynamics of the AI era, Türkiye’s legal and regulatory framework, and practical strategies organizations can adopt.

1. AI-Driven Threats: New Risk Dynamics

AI-enabled attack techniques are significantly reshaping the nature and scale of cyber threats. Identity-based attacks, in particular, have become one of the fastest-growing threat categories in recent years. AI-powered tools enable phishing and social engineering attacks to be highly personalized, making them more convincing and harder to detect, thereby reducing the effectiveness of traditional prevention mechanisms.

In parallel, polymorphic malware—malicious software capable of continuously altering its structure—has evolved to bypass conventional antivirus solutions. These attacks highlight the limitations of signature-based detection methods and reinforce the need for behavioral analysis and adaptive security models. The speed and flexibility provided by AI on the attacker side require organizations to reassess how they define, monitor, and respond to cyber risk.

2. AI on the Defensive Side: From Reactive to Preventive Security

While AI intensifies the threat landscape, it also introduces powerful capabilities for defense. Traditional, incident-driven security models are increasingly giving way to more predictive and preventive approaches.

2.1. Anomaly Detection

Modern AI-based security solutions can identify unusual access patterns or network traffic within seconds by learning from historical behavioral data. This capability is particularly valuable in complex environments, such as encrypted traffic, where visibility is limited. Techniques such as federated learning—which enables collaborative model training without centralizing data—and continuous learning enhance threat detection while preserving data privacy.

2.2. Automated Response and SOAR Platforms

AI-enabled Security Orchestration, Automation and Response (SOAR) platforms go beyond alerting by enabling automated actions based on risk scoring. High-priority incidents can be addressed quickly, while false positives are reduced, supporting both operational efficiency and business continuity.

2.3. Predictive Analytics and Zero-Day Risks

By learning from historical attack data, AI models can support predictive analytics and “what-if” scenario modeling to anticipate emerging threats, including previously unknown zero-day vulnerabilities. This approach positions cybersecurity as an integral component of strategic risk management rather than a purely operational function.

2.4. Human Oversight and Explainable AI

Despite the advantages of automation, human expertise remains indispensable. As a result, modern security architectures increasingly rely on Explainable AI (XAI) principles, ensuring that automated decisions can be interpreted and reviewed by security professionals. This transparency reduces the risk of context-blind responses and strengthens trust in AI-driven security systems.

3. Legal and Regulatory Framework in Türkiye

3.1. National Cybersecurity Strategy

Türkiye’s National Cybersecurity Strategy emphasizes the adoption of a Zero Trust security model, which assumes no implicit trust based on network location or user role. Under this approach, every access request is continuously verified. Zero Trust has become particularly critical in environments shaped by hybrid and remote working arrangements.

3.2. Data Protection and AI-Related Guidance

Guidance issued by the Turkish Data Protection Authority highlights key principles for AI-based systems, including human oversight, transparency, purpose limitation, and data minimization. This framework reinforces the view that cybersecurity is not only a technical requirement, but also an ethical and legal responsibility closely tied to personal data protection obligations.

4. Practical Organizational Strategies

4.1. Risk Mapping and AI-Based Monitoring

AI-driven risk mapping enables organizations to identify early warning signals through anomaly detection and behavioral analysis. This supports more informed prioritization of risks and more effective allocation of security resources.

4.2. Human-Centric Security Awareness

Since a large proportion of identity-based attacks stem from human error, security awareness initiatives play a critical role. These efforts should extend beyond IT teams to include departments such as HR, legal, and finance, strengthening organization-wide resilience.

4.3. Data Access and Protection Policies

Limiting data access in line with the principle of least privilege, combined with end-to-end encryption and real-time monitoring, has become a foundational requirement for both cybersecurity and compliance with data protection regulations.

4.4. Identity and Access Management

Multi-factor authentication and regular identity audits help reduce the risk of unauthorized access while preserving the integrity of internal systems.

4.5. AI Governance and Ethical Oversight

Effective AI governance requires regular assessments of bias, data drift, and fairness. Establishing clear accountability mechanisms aligned with transparency and responsibility principles is essential for sustainable AI deployment.

4.6. Collaborative Incident Response

Testing incident response plans through AI-based simulations and exercises improves organizational preparedness. Coordination with public authorities and business partners further strengthens resilience in the face of complex cyber incidents.

Conclusion

The widespread adoption of AI-enabled technologies is transforming cybersecurity into a more dynamic, less predictable, and increasingly multi-dimensional domain for organizations. In this evolving risk environment, cybersecurity can no longer be addressed solely through technical controls; it has become a governance issue closely linked to risk management, regulatory compliance, and strategic decision-making. For this reason, approaching cybersecurity in the age of artificial intelligence requires moving beyond reactive responses to isolated incidents and embracing a proactive, sustainable, and organization-wide perspective.

References

• IBM (2025). X-Force Threat Intelligence Index 2025. https://www.ibm.com/reports/threat-intelligence
• Falade, P. V. (2023). AI-based Voice and Text Impersonation Models. https://arxiv.org/abs/2310.05595
• ENISA (2024). ENISA Threat Landscape 2024. https://www.enisa.europa.eu/publications/enisa-threat-landscape-2024
• WEF (2025). Global Cybersecurity Outlook 2025. https://www.weforum.org/publications/global-cybersecurity-outlook-2025
• OECD (2024). Digital Security Risk Management. https://www.oecd.org/en/topics/digital-security-risk-management.html
• Ministry of Transport and Infrastructure of Türkiye (2024). National Cybersecurity Strategy and Action Plan 2024–2028. https://www.uab.gov.tr/uploads/pages/siber-guvenligin-yol-haritasi-yerli-ve-milli-tekno/ulusal-siber-guvenlik-stratejisi-2024-2028.pdf
• KVKK – Personal Data Protection Authority of Türkiye (2024). Guidelines on the Protection of Personal Data in the Field of Artificial Intelligence. https://www.kvkk.gov.tr/SharedFolderServer/CMSFiles/25a1162f-0e61-4a43-98d0-3e7d057ac31a.pdf