Traditional Cybersecurity vs. AI-Powered Cybersecurity

This article examines the differences between traditional cybersecurity and AI-powered cybersecurity approaches. The fundamental differences between the two methods are discussed in detail and explained through example scenarios.

Traditional cybersecurity generally refers to a defensive approach that includes techniques such as network security, access controls, and firewalls to protect systems from attackers. In contrast, AI-powered cybersecurity aims to detect and respond to potential threats quickly and effectively by combining machine learning with algorithms.

The table below compares these two approaches within a set of defined criteria and provides a summary. Following the table, each criterion is discussed in detail, accompanied by example scenarios.

Comparison in Terms of Threat Detection

Identifying and stopping attacks at an early stage is critically important in cybersecurity. The main differences between traditional and AI-powered approaches are discussed below:

1. Threat Identification Method: The traditional method uses rule-based or signature-based analyses to detect threats according to predefined formats and requires manual updates for new threats. In the AI-powered method, abnormal activities can be detected in real time using machine learning and behavioral analysis techniques.

Advantage of AI: It can proactively identify new and unknown threats and is not limited to known ones.

Advantage of the traditional method: It provides fast, resource-efficient, and predictable protection against known threats.

2. Sensitivity in Threat Detection: While the traditional method operates based on specific rules, it may produce false positives or overlook certain threats. This can extend the response time and delay the reaction to threats. In AI-assisted cybersecurity, system and user behaviors are analyzed, which can provide more realistic results.

Advantage of AI: It can reduce false positives, shorten the response time, and automatically generate real-time reactions.

Example Scenario: In a traditional system, an attacker identifies a Zero-Day vulnerability on the target system and installs malicious software. On a server within the network, this malware runs in the background, collecting sensitive customer information. The attacker has made the malware polymorphic, so traditional security software fails to detect the attack. This process may continue for some time or be stopped only if the security team notices it.

If the system is AI-assisted, artificial intelligence analyzes network traffic and determines normal access patterns. It detects unusual command exchanges from the running software, identifies it as a potential attack, and instantly activates defense protocols. In this way, the attack attempt is detected at an early stage and is stopped.

*Polymorphic: constantly changing and morphing (e.g., Storm Worm).

Comparison in Terms of Authentication

The primary importance of authentication is to prevent unauthorized access by attackers, thereby protecting systems and preserving data integrity. The main differences between the traditional and AI-supported approaches are discussed below:

1. Authentication Methods: Traditional authentication relies on methods such as passwords, PINs, cards, and tokens, whereas AI-supported authentication enhances security to a dynamic and higher level through behavioral analysis and biometric techniques.

Advantage of AI: Biometric authentication is based on physical and behavioral traits unique to the individual, making it harder to obtain or replicate.

2. User Experience and Security Level: In traditional authentication methods, data can be stolen, copied, or guessed; passwords can be forgotten, and manual verification processes can be time-consuming. In AI-supported authentication, instant identity verification accelerates the process, while the user’s physical or behavioral traits are continuously analyzed to prevent identity fraud.

Advantage of AI: User experience improves, authentication processes become faster, and the system reaches a more secure level against identity theft.

Example Scenario: In a traditional authentication system, an attacker obtains the identity credentials (username and password) of a company employee through phishing. They log into the system using these credentials, and if the system only checks the credentials, it recognizes the attacker as an authorized user. The attacker can then carry out activities on the system according to the user’s privileges (such as data leaks, exploiting other potential vulnerabilities in the system, etc.).

If the same scenario occurs in an AI-supported authentication system, the attacker again obtains an employee’s credentials through phishing. They attempt to log in but encounter an additional security step: fingerprint identification. As a result, biometric verification prevents the attacker from succeeding.

Let’s now imagine that instead of biometric verification, the system uses a traditional multi-factor authentication method—an additional OTP step. The attacker now has two options: bypass the OTP or perform SIM swapping. The attacker succeeds in the additional step and gains access to the system.

The AI-supported system then begins analyzing the login based on the user’s previous login patterns and behaviors. The login does not align with the user’s usual activity times, device information, location, or behaviors on the system (e.g., keyboard and mouse movements, large-scale data downloads, etc.). These anomalies raise suspicion.

The system detects the suspicious activity, terminates the session, blocks the account, and automatically alerts the security team. As a result, the attacker’s attempt ends before they can cause any impact on the system.

Additional Information: How does Artificial Intelligence analyze user behavior?

It analyzes user behavior using advanced data processing techniques and algorithms. A few examples include:

- Behavioral Biometrics: Examines screen usage, keyboard, and mouse habits.
- Machine Learning Models: Utilizes past data to predict potential scenarios.
- Natural Language Processing (NLP): Analyzes emotions, intent, tone of voice, and messaging history.
- Anomaly Detection: Learns normal behaviors and detects abnormal activities.
- Big Data Analytics: Examines digital footprints (such as transaction history, interests, click patterns, etc.).

Comparison in Terms of Malware Analysis

Malware analysis is carried out to minimize damage and detect threats in order to protect systems. The fundamental differences between traditional and AI-assisted approaches are outlined below:

1. Analysis Method and Detection Time: Traditional analysis methods are signature-based and operate by comparing data from previously identified threats with databases in a static manner. Alternatively, the software can be executed in a virtual environment where its behavior is dynamically observed. However, when a new threat emerges (e.g., a zero-day attack), traditional methods may prove inadequate.

Real-time AI-assisted analyses examine software activities directly on the system. In this way, anomalies can be instantly detected even when unknown threats are involved.

Advantage of AI: Enables real-time detection of new, unknown, and complex attacks, and can shorten response time.

Advantage of traditional methods: Offers benefits in terms of reliability, explainability, and ethical considerations.

2. False Positive Rate: In traditional analyses, harmless files may sometimes be mistakenly flagged as threats, leading to unnecessary interventions. In AI-assisted analyses, the AI examines how the file behaves and can more accurately identify real threats and respond accordingly.

Advantage of AI: False positives are minimized, thus eliminating unnecessary interventions.

Example Scenario: The attacker uses "Living off the Land" (LoTL) techniques targeting the system and sends a file containing malware to the target user via email. Let's assume the user has low information security awareness. The user interacts with the file, and the malware gets downloaded.

Thanks to the attack technique used, the malware modifies DLL files that are used by legitimate applications, allowing it to run persistently. Traditional security software interprets this as a legitimate activity, and the attacker is able to operate at their current privilege level.

In AI-assisted systems, it is detected that illegitimate processes are attempting to maintain persistence on the system. This is identified through behavior-based anomaly analysis on the system. The attack is detected at an early stage, and a malware signature is created to help prevent similar attacks in the future.

Comparison in Terms of Speed and Responsiveness

In cybersecurity, speed and response are among the most fundamental pillars. With attacks becoming more complex each day, fast detection, early response, isolation, action, and notification processes must function correctly. These are crucial not only for minimizing the impact of the attack but also for managing legal processes. The main differences between traditional and AI-assisted approaches are outlined below:

1. Time to Detect Threats: In traditional methods, detecting an attack often requires manual review and can take minutes, hours, days, or even months. Thanks to machine learning and behavioral analysis, threats can be detected instantly, and AI can automatically respond to these abnormal activities in a very short time.

Advantage of AI: Real-time detection and fast response enable effective process management.

2. Post-Incident Analysis and Adaptation: In the traditional approach, attacks are analyzed manually, and updating systems takes time. In AI-supported systems, continuous learning allows for automatic updates after an attack and helps to prevent future threats.

Advantage of AI: Continuously improves and becomes more effective against future attacks.

Advantage of traditional methods: Provides detailed documentation and manual control, refreshes institutional memory, and offers advantages in accountability and legal traceability.

Example Scenario: In a traditional system, an attacker infiltrates a company's network through a phishing attempt. Due to the lack of additional security measures, the security team only detects the situation a few days later. During this time, the attacker manages to steal a significant amount of sensitive data. When the same attack occurs in an AI-assisted system, the AI instantly analyzes the suspicious email and blocks it, rendering the attack unsuccessful. (The attack process in this example scenario also applies to the example scenario under the “Authentication” criterion.)

Comparison in Terms of Scalability

The ability of a structure or system to provide scalability and continuity in the face of growing volume, increasing number of users, the need to process big data, efficiency expectations, and security threats is critically important for organizations in terms of information security. The main differences between traditional and AI-assisted approaches are outlined below:

1. Resource Utilization – Efficiency: Traditional methods require more physical infrastructure and human resources as new threats emerge or existing threats manifest through more frequent attacks. Artificial intelligence continuously improves its ability to manage large systems more effectively by reducing the need for human intervention.

Advantage of AI: Increases operational efficiency and reduces costs by making security autonomous.

Advantage of the traditional method: Since the processes are human-centered, there is greater flexibility in resource use and a higher capability for immediate intervention.

2. Data Processing Capacity: Traditional security systems operate with a limited amount of data. As the number of threats increases and new ones appear, analyzing, processing, validating, and acting on this data becomes time-consuming. AI-supported systems can process information produced by many users and devices in real time by working with big data, learn new threats quickly, and adapt themselves as the scale increases. As a result, security processes become faster and more effective.

Advantage of AI: Quickly adapts to emerging threats, optimizes itself, and can analyze massive data sets to handle large structures.

Advantage of the traditional method: Since AI systems are open to learning, if trained with poor data, they may make mistakes. Traditional systems, on the other hand, operate based on fixed rules and authorities until updated.

3. Effectiveness for Large Structures and Global Networks: In traditional methods, security teams may act regionally for certain threats, but taking simultaneous global action is more difficult. Since AI-supported systems operate automatically, they can analyze threats instantly in large-scale, globally distributed networks and generate instant responses for all systems.

Advantage of AI: Enables faster action in the face of global-scale attacks.

Example Scenario: A company with an infrastructure that includes over 2,000 microservices running on Kubernetes and data centers across three continents becomes the target of an attacker. The attacker creates an e-invoice intended for employees in the accounting department and sends it via email. The email contains what appears to be a .pdf file but is actually a file with a hidden macro. In a traditional system, this email passes through the mail server and reaches the user, who interacts with it. When the file is opened, it leaves a backdoor via PowerShell. The backdoor connects to the attacker's control panel and begins collecting information about the target system. Since the traffic on the network is encrypted, the IDS (Intrusion Detection System) misses this activity.

The attacker collects credentials and attempts lateral movement between servers (using techniques such as kerberoasting, pass-the-hash, etc.). The attacker redirects the DNS server of the compromised domain to their own server. The stolen data is encrypted and hidden within DNS queries, which are then sent in fragments. These queries reach the attacker's server via DNS resolvers, with queries and responses exchanged back and forth. Traditional firewalls perceive this activity as legitimate DNS traffic.

Due to the presence of separate security teams at each of the three data centers, the events occurring at different time intervals, and the fact that DNS traffic is generally less scrutinized, detecting this attack and managing it through coordinated collaboration takes time.

In AI-supported systems, the mail server at the very beginning can analyze behavioral anomalies in the email and quarantine it. If this stage is bypassed, the system can detect abnormal DNS traffic patterns on the network that do not match normal behavior and isolate the server. The suspicious data flow is stopped. Speed and response are achieved automatically and on a large scale.

Comparison in Terms of Learning Ability

In cybersecurity, the ability to learn is a critical factor in combating threats, as cyber threats are constantly evolving. A security system without learning capability remains static and becomes vulnerable to emerging threats. The fundamental differences between traditional and AI-supported approaches are discussed below:

1. Source of Information and Data Processing – Pattern Recognition and Anomaly Detection – Response and Adaptation: Traditional systems analyze data based on predefined static rules and known threats. Security strategies are defined and implemented by experts. On the other hand, AI-supported systems can identify complex patterns within large volumes of data, predict the unexpected, and intervene automatically.

Advantage of AI: Its ability to dynamically detect threats and adapt to threat environments enables early and automatic responses to attacks.

Example Scenario: A company’s firewall protects the system against known malware. However, this traditional approach requires manual intervention to identify a new type of attack when it emerges. If the same system is AI-supported, it can detect unusual behavior as an unknown activity and automatically adapt to eliminate the potential threat. (The attack stages in the scenario given under the “Malware Analysis” criterion are also applicable here.)

Additional Information: What are the challenges and risks AI faces in cybersecurity?

- Data privacy: The necessity of collecting large amounts of data and ensuring its protection.
- Deceptive attacks: Attackers may use fake data to mislead AI systems.
- AI-powered attacks: Attackers may leverage AI themselves to design more complex and sophisticated attacks.