AI in Cybersecurity: A New Frontier or a Threat?
- ESKA ITeam
- Apr 24
- 3 min read
Artificial Intelligence (AI) is rapidly becoming one of the most powerful tools in the field of cybersecurity. With its capabilities in machine learning, big data analysis, and automation of routine tasks, AI is transforming how organizations protect their information systems. But is the implementation of AI in cybersecurity purely beneficial? Or does this technology bring new risks that could intensify existing threats?
In this article, we will explore:
how AI is used to protect data,
what opportunities it offers to cybersecurity professionals,
and what challenges we must prepare for in the near future.
How AI Is Transforming Cybersecurity Approaches
1. Automated Threat Detection
AI enables the creation of systems that autonomously analyze network traffic, user behavior, and security events in real time. These systems can identify anomalies that indicate potential attacks far more quickly and accurately than traditional antivirus software or manual reviews.
Example:
XDR and SIEM solutions with AI-powered analytics (e.g., SentinelOne, Exabeam) automatically detect suspicious behavior patterns without human intervention.
2. Attack Prediction
By analyzing historical incident data, AI can predict potential attack scenarios, detect vulnerabilities, and suggest proactive security measures.
Example:
Platforms like Darktrace use machine learning to establish a “normal behavior” baseline for network activity, enabling them to detect anomalies before an attack begins.
3. Automated Incident Response
AI can autonomously initiate certain responses to incidents, such as:
blocking traffic
isolating infected systems
triggering backup recovery.
Example:
In some SOC as a Service solutions, AI handles initial incident triage, allowing analysts to focus on more complex threats.
New Opportunities Brought by AI in Cybersecurity
Scalability: AI can process data from thousands of sources simultaneously, making it indispensable for large organizations.
Faster Response Time: AI systems react to threats instantly, eliminating human delay.
Advanced Data Analysis: AI detects hidden patterns in massive datasets, uncovering complex attack indicators.
Cost Optimization: Automation reduces the need for large security analyst teams, lowering operational costs.
Leading Vendors Integrating AI into Cybersecurity
Many modern companies are embedding AI into their products to enhance cyber threat defense. Here are several key players and examples of their AI capabilities:
Exabeam: Uses machine learning to build behavioral profiles of users and devices (UEBA). It detects deviations from normal activity, highlighting potential threats such as insider attacks and credential theft.
LogRhythm: Integrates AI into its SIEM platform to automatically correlate security events, detect complex attacks, and reduce false positives. AI helps prioritize incidents based on risk.
SentinelOne: Employs autonomous AI agents for behavior-based threat detection on endpoints (EDR/XDR). The Singularity XDR platform automatically identifies, isolates, and neutralizes attacks — even zero-day threats — without human input.
CyberArk: Applies AI to enhance privileged access management (PAM). It analyzes administrator behavior and automatically detects suspicious actions like unusual login attempts or access to critical systems at odd hours.
Segura: Uses AI to scan web content in real time. Their platform detects phishing websites, malware, and other threats before users interact with them.
Palo Alto Networks (NGFW): Integrates AI into its Next-Generation Firewall and Cortex XDR platform for:
Intelligent traffic filtering
Malicious behavior detection
Early-stage attack prediction and prevention
AI in Cybersecurity: Opportunities vs. Risks
What Risks Does AI Pose in Cybersecurity?
1. Attacks on AI Itself
AI systems can become targets of specialized attacks such as:
Data poisoning: corrupting training data
Adversarial attacks: generating false incidents
Exploitation of decision-making logic
Example:
Hackers might intentionally inject flawed data into an AI model’s training process, causing it to misclassify threats as safe behavior.
2. Overreliance on Automation
Fully automated processes without human oversight can result in critical errors. AI still lacks the ability to interpret context accurately, especially in complex Advanced Persistent Threat (APT) scenarios.
3. Malicious Use of AI by Hackers
Cybercriminals are already leveraging AI to:
Automate phishing attacks
Create adaptive malware in real time
Scan for vulnerabilities in applications
Example:
AI-powered tools generate highly personalized phishing emails that are harder to detect using traditional methods.
How to Minimize AI-Related Risks in Cybersecurity
Use a hybrid approach: combine AI analytics with manual validation for critical decisions.
Perform regular security model testing to identify vulnerabilities.
Ensure data quality control during AI model training.
Continuously update security systems to address emerging threat scenarios.
AI offers unprecedented opportunities to enhance cybersecurity — helping to detect threats faster, respond more effectively, and reduce the burden on security teams. However, these capabilities come with significant risks: misusing AI can create new attack vectors rather than closing existing gaps.
That’s why successful implementation of AI in cybersecurity requires a balance between automation, human control, and continuous development of defensive strategies.
At ESKA Security, we specialize in selecting and integrating cutting-edge cybersecurity solutions powered by AI. We know where AI can be truly useful and effective. Contact our experts for tailored advice.
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