Cybersecurity in the Age of Artificial Intelligence

In today’s rapidly evolving digital age, the emergence of artificial intelligence has led to fundamental changes in the field of cybersecurity. Artificial intelligence plays a significant role in both defensive strategies and attack methods.

Within defensive strategies, it is used in areas such as detecting anomalies, predicting attacks in advance, responding to them quickly, and building more robust defense mechanisms; on the attack side, it is used for malicious activities such as identifying vulnerabilities and executing attacks using more advanced techniques.

Below, the methods, areas of use, and dual-sided impacts of artificial intelligence in cybersecurity are examined.

The Use of Machine Learning Methods in Cybersecurity

Supervised Learning: This method enables AI engines to learn specific behavioral patterns through large, pre-labeled datasets. By learning from applications that contain vulnerabilities, artificial intelligence can recognize such weaknesses. After this learning process, it can demonstrate the ability to detect vulnerabilities without analyzing the behavior of the executed code.

For example, in phishing emails, a model trained with malicious emails using supervised learning can determine whether new emails are safe. This technique can be used by cybersecurity professionals who aim to enhance defense strategies and security, as well as by malicious actors to quickly identify an organization’s vulnerabilities. In addition, cyber attackers can use AI-powered tools to create more convincing and personalized phishing emails.

Unsupervised Learning: This method enables models to learn from unlabeled and unclassified data. Cybersecurity professionals and attackers can use this approach to develop automated tests that identify vulnerabilities that are difficult to find manually.

Reinforcement Learning: This method uses experience-based learning and reward mechanisms to develop optimal action strategies. For example, an application using reinforcement learning in cybersecurity may be an automated attack detection and response system. Such systems experience various cyber-attack scenarios and improve their detection strategies based on the feedback received.

As an example, the machines that participated in the “Cyber Grand Challenge” (a competition organized by DARPA under the U.S. Department of Defense, where participant systems compete in a virtual environment by automatically finding vulnerabilities, exploiting them to attack other systems, and protecting themselves) used reinforcement learning to optimize resource utilization.

Areas of Use of Artificial Intelligence in Cybersecurity

Threat Detection: Artificial intelligence helps identify malicious activities such as abnormal data access events, malware, phishing attacks, and insider threats more quickly. For example, an AI-based email filtering tool can rapidly detect high-volume and similar email campaigns to block spam attacks.

Threat Discovery:By monitoring assets and analyzing abnormal activity, AI uncovers potential threats, such as insider threats, far faster and more comprehensively than humans can.

Alert Triage and Prioritization: Instead of being overwhelmed by thousands of alerts, security teams can automatically classify and prioritize them based on factors such as attack type, frequency, and previous experience. Cybersecurity platforms analyze similar past incidents to determine which alerts carry higher risk. For example, a SIEM system can highlight recurring and critical alerts, allowing security teams to focus on the most urgent threats.

Targeted Investigation and Support: AI enables fast and efficient investigation of incidents based on current and historical data by analyzing similar past events. For example, a SIEM tool can analyze IP addresses associated with previous data breaches to detect similar threats originating from those addresses.

Cyber Risk Identification: New risk categories, increasing use of social media, and widespread digitalization pose risks for organizations. Artificial intelligence identifies or predicts risks that are difficult for humans and rule-based systems to detect.

Threat Hunting: AI ingests known tactics, techniques, and procedures (TTPs) and attack patterns to analyze threats and rapidly search for new ones, enabling proactive detection.

Configuration Review: AI uses bots to review system configurations to prevent misconfigurations. For example, a configuration management tool can analyze firewall rules to identify and correct misconfigured policies.

Attack Path Modeling: By applying predictive analytics to security data, AI determines potential attack paths, forecasts possible attack scenarios, and identifies weak points. For example, a malware analysis tool can model how malware spreads to determine potential targets and strengthen protection strategies.

Artificial Intelligence on the Attack Side

On the offensive side of cybersecurity, artificial intelligence is advancing rapidly in parallel with developments in defense. AI-driven methods are more effective and effortless than traditional approaches. By analyzing large volumes of data, they can automatically identify weak points. As a result, attackers can quickly find vulnerabilities, automate their attacks to exploit them, and modify malware in ways that evade traditional antivirus detection.

Emails that once contained poor grammar and artificial wording have largely disappeared with the rise of tools such as ChatGPT. Attackers now create highly convincing, natural-sounding, and multilingual phishing messages tailored to individual targets. In addition to email, deepfake technology enables the imitation of people’s voices and images to request sensitive information.

Artificial Intelligence: A New Attack Surface

Artificial intelligence today is no longer just a tool for detecting cyber threats. It has become an active system component that determines access permissions, enforces security policies, and executes automated responses. This transforms AI into a new class of digital asset that must be protected. A model that is improperly trained, subjected to data poisoning, manipulated in its outputs, or exposed to unauthorized access can lead to consequences as severe as a traditional network breach. In other words, modern cybersecurity must focus not only on using AI to protect systems, but also on protecting AI systems themselves.

As a result, artificial intelligence has become a force that fundamentally reshapes both the defensive and offensive sides of cybersecurity. However, as AI becomes an integral part of security infrastructure, it also emerges as a new source of risk and a new attack surface. For this reason, organizations must not only use AI to detect threats, but also ensure that the AI systems they rely on are securely governed and managed. Natica’s cybersecurity approach also addresses this secure AI perspective across the data, model, and access layers.