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AI and Cybersecurity in 2025: How AI Model Advances Reshape Digital Risk

AI and Cybersecurity in 2025: How AI Model Advances Reshape Digital Risk

Dr. Said Salloum Artificial Intelligence

Artificial intelligence and cybersecurity are developing at breakneck speed, with many potential applications already implemented in everyday working life (Mohamed, 2025). You witness new tools and threats, new regulations. Businesses implement AI models to boost productivity as well as shave costs.

At the same time, attackers are taking advantage of these models to develop novel cyber-risk. This leads to a steady pressure to innovate and remain safe. What follows is components focused on the most prominent dynamics unfolding in this environment.

1.1 Acceleration of AI Adoption

AI is not anymore restricted to trivial purposes. Now you’re seeing it applied to education, to health care, to finance, transportation and the government (Kaplan, 2020). Generative AI platforms such as large language models are now prevalent in analytics, automation and customer support (Parasuraman, 2024). For example, over 60 percent of companies increased investment in AI-based programmes in last year according to (Bughin et al., 2017). This scalability enhances cost efficiency but raises the risk of system-wide failure and inappropriate use of data.

1.2 Expanding Cybersecurity Threat Surface

As AI systems expand, so does the attack surface. Cyber-attacks are increasing in frequency and sophistication. “Seventy-five percent increase in adversaries leveraging AI-driven automation for malware” there was an increase of 75% increase in the use (CrowdStrike, 2024). Now you face attacks on training data, model parameters, cloud-based inference services and supply chains.

And you see attackers applying AI to accelerate reconnaissance, generate phishing content and evade standard security filters as well. This change renders traditional defense methods more-and-more ineffective.

1.3 Rise of AI-Targeted Attacks

Threat actors now specifically attack AI models themselves. Several forms of AI-specific threats are rising.

  • Prompt injection.
  • Model extraction.
  • Data poisoning.
  • Adversarial perturbations.

One recent study, (OpenAI, 2023) pointed out how adversarial treatment of foundation models could facilitate misinformation, impersonation, and posting model abuse. The 2023 threat landscape from ENISA reinforces the trend, citing a doubling of AI incidents across Europe.

1.4 Increasing Dependency on AI-Driven Cyber Defense

Cybersecurity platforms now use machine learning for anomaly detection and real-time monitoring. These systems help detect threats faster and reduce analyst workload. But attackers are learning to exploit them. A (IBM, 2025) report describes cases where adversaries generated synthetic “normal” behavior to fool AI-based intrusion detection. This weakens defensive systems and increases false negatives.

You also see deepfake-based attacks growing. Criminals clone voices or video to commit fraud and access internal systems. The FBI issued multiple 2023–2024 warnings about deepfake-enabled identity attacks targeting corporate environments.

1.5 Regulatory and Governance Push from 2023–2025

Governments are strengthening oversight. The (Intelligence, 2024) grades AI systems by risk and demands extensive documentation and supervision. The NIST AI Risk Management Framework (NIST, 2024) helps businesses, developers, and operators navigate responsible AI-development practices. GCC countries such as the UAE and Saudi Arabia issued new cybersecurity frameworks that highlight auditing AI systems and data conservancy.

These regulations require organizations to:

  • Review AI risks.
  • Maintain transparency.
  • Protect training datasets.
  • Track decision-making processes.

Many companies struggle with these requirements because they lack AI governance structures or rely heavily on vendors.

1.6 Why Institutions Must Balance Innovation and Security

As institutions quickly adopt AI there are efficiency-gains, but they also open themselves to a new class of risks. The balance of performance and governance is being disrupted for leaders. AI pipelines must be made secure, with sensitive user data able to be protected and eavesdrop-resistant. That balance will shape how effectively institutions are able to scale AI through 2025.

In summary, AI systems are advancing too quickly for most institutions to adapt. You watch as new models, bigger data sets and stronger automation come into every industry you are living through. At the same, time cyber threats are getting more sophisticated as attackers figure out how to take advantage of tools like these. This is a future in which innovation and risk are inseparable. Companies require approaches to both deriving value from AI while minimizing the liabilities that accompany it.

The priority list for institutions will be shaped over the next two years. They need to reinforce governance to ensure that every AI system has clear responsibilities. They will need to create security measures guarding their training data, model outputs and cloud infrastructure. And they need to merge traditional cyber defense with new deflection strategies like model auditing, adversarial testing and continuous monitoring. Studies from NIST (2023), ENISA (2023) and IBM Security (2024) all indicate that these practices mitigate exposure to AI-based attacks.

You see growing regulatory pressure as well. The EU AI Act, GCC national cybersecurity frameworks and international standards are setting the bar for transparency, documentation and risk assessment ever higher. Companies that get a head start have a competitive advantage because they win trust from both users, clients and regulators. It is now up to leadership commitment.

Institutions must invest in skills, revise internal policies and train teams to deal with AI-enhanced phishing, deepfakes and data manipulation. They need to see that we don't allow security reviews to be short-circuited in the interest of getting it done quickly. This trade-off of speed and security is rapidly becoming a strategic imposition, rather than simply a technical trade-off. AI will continue to alter operations, decision-making and cybersecurity.

Those businesses that combine responsible AI practices and strong security bases will be better prepared to navigate emerging risks. This encourages long-term resilience and provides the ability for organizations to innovate with confidence in a world where the threat landscape can pivot daily.

References

  • Bughin, J., Hazan, E., Sree Ramaswamy, P., DC, W., & Chu, M. (2017). Artificial intelligence the next digital frontier.
  • CrowdStrike. (2024). 2024 Global Threat Report. https://go.crowdstrike.com/rs/281-OBQ-266/images/GlobalThreatReport2024.pdf
  • IBM. (2025). IBM X-Force 2025 Threat Intelligence Index. https://www.ibm.com/thought-leadership/institute-business-value/en-us/report/2025-threat-intelligence-index
  • Intelligence, E. A. (2024). Official act text and summary. https://data.europa.eu/eli/reg/2024/1689/oj
  • Kaplan, J. (2020). Humans Need Not Apply: A Guide to Wealth & Work in the Age of Artificial Intelligence. Yale University Press.
  • Mohamed, N. (2025). Artificial intelligence and machine learning in cybersecurity: a deep dive into state-of-the-art techniques and future paradigms. Knowledge and Information Systems, 1–87.
  • NIST. (2024). Generative AI Cross-Sectoral Profile (AI RMF companion). https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.600-1.pdf
  • OpenAI. (2023). Forecasting potential misuses of language models for disinformation campaigns and how to reduce risk. https://openai.com/index/forecasting-misuse/?utm_source=chatgpt.com
  • Parasuraman, B. (2024). Introduction to generative AI and large language models (LLMs). In Mastering spring AI: the java developer’s guide for large language models and generative AI (pp. 1–34). Springer.