How To Make AI And Cybersecurity Work Together

In the contemporary age of digitalization, with continuously emerging cyber-attacks, the convergence of artificial intelligence and cybersecurity offers new opportunities to protect information systems. The application of AI, particularly through an AI orchestration platform, not only automates repetitive tasks but also improves the efficiency of threat detection and response.

Traditional systems are no longer adequate

Legacy approaches to cyber defense depended on known patterns of attack. They are highly effective in wars against repeat threats, but they are no longer useful in wars against new attacker technology. Zero-day attacks, like deepfake phishing attacks, or adaptive behavior in real-time botnets are all outside legacy solutions.

Artificial intelligence can identify behavioral anomalies even without pre-existing signatures. It can therefore stop new threats at an early stage. When the human or legacy system cannot perceive a threat, the algorithm identifies 'noise' in data and raises an alarm.

AI speeds up incident response

Detection of the threat is half the battle. The most crucial thing is to react in time before the damage is done irreversibly. New attacks can be launched in minutes, and even in seconds. For example, ransomware can encrypt a company server or database within 5 minutes. In this case, a manual response is too late.

This is where cyber defense matures with the help of artificial intelligence. You can react automatically on detecting threats: close ports, terminate sessions, lock accounts, develop response scenarios (playbooks) the system runs without any operator and ongoing self-improvement through reviewing previous event outcomes and refining the response. It reduces the "window of vulnerability" to a great degree, reduces the quantity of losses, reduces experts' workload and reduces the human factor. This is especially helpful to big organizations, where dozens of threats or incidents simultaneously show up in various areas of the network.

AI is not an add-on module of the security architecture. It is a cyber reflex of an organization reacting instantaneously to an attacking threat when the log file is being loaded by the analyst.

AI and human are not competitors, but allies

AI analyzes vast quantities of data at dizzying speeds, searches for patterns, and responds to them in seconds. But in a vacuum, no moral compass, and no concern for political or reputational fallout from decisions. A human has adaptive thinking, strategic vision, and know-how that cannot be replicated by an algorithmic system.

A 21st-century security team is like this:

• AI identifies and defends against potential threats automatically;
• An analyst reviews an occurrence within a broader context (regulatory, commercial, social);
• A strategist takes decisions that are then carried out later.

This is not a model of substitution, but cooperation. AI segregation from humans is either over-autoing (with room for error) or overburdening workers with human work. Only human and AI collaboration can effectively combat deep, multi-layered threats. With AI, AI is a black box, and with human, an expert is left single-handedly with a flood of data.

Cybersecurity economics. AI saves resources

AI arrival is not a fashion, but a cost-saving investment that yields dividends within a few months.

The primary cost savings areas are:

• lowering incident response costs;
• avoiding fines for violating data storage laws (e.g. GDPR);
• utmost personnel without compromising on efficiency;
• shorter downtime, faster restoration.

These are especially important for sectors processing huge volumes of data, such as banking, energy, and healthcare.

Issues of integrating AI into cybersecurity systems

Integrating AI into cybersecurity has a number of challenges:

• There is, first of all, the black box problem, where algorithmic solutions turn into enigmas for experts.
• Second is dependence on the quality of data. Malicious actors can train AI models to be susceptible to poisoning of their training sets.
• Third, the dynamic nature of cyber threats implies there is a need for continuous updating of the model.
• The fourth is ethical implications of using AI in security surveillance, especially to user privacy. Despite these challenges, a balance is required to bring technological innovation together with strong ethics and regulatory responsibility.

Synergy of AI and cybersecurity. effective cooperation

AI must be paired with cybersecurity under a clear plan. AI scans data and picks up anomalies, and experts work through this in the context of a threat. To work together, there must exist certain interfaces that receive algorithmic inputs and translate them into usable recommendations for experts. It also involves training the security team in the basics of how AI works so that they can interpret the output effectively and configure the models according to the organization's specific requirements.

Case studies. Operating within real systems

These real-life instances illustrate how companies leverage AI to support cybersecurity and reduce threat:

• Microsoft Defender (2023): over 1.5 million cloud environment suspicious events prevented by behavior analysis.
• Deutsche Bank (2022): server lock-down automatically triggered when an internal anomaly is detected for a temporary duration of 1.2 seconds.
• Israeli Bank (2022): AI-based predictive module detected unwanted activity in the API and prevented an attempt at a hack even before it was attempted.
• Polish energy industry: Prevented more than €3 million in cyber attack costs through the implementation of a self-learning analytics system.
• US telecommunication firm: 82% decrease in SOC team workload following the implementation of AI-based event filtering.

Together, these cases demonstrate how AI, and specifically through an orchestration platform, allows organizations to react faster and smarter to threats.