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Artificial intelligence in cybersecurity:
usage scenarios, trends & best practices
September 25, 2025
Adopting AI in cybersecurity helps organizations identify and resolve vulnerabilities in their IT systems, detect cyber threats before they escalate into breaches, and quickly recover from security incidents to prevent further disruptions.
Beyond these immediate benefits, AI also delivers strategic advantages when applied to areas like cyber threat intelligence. To realize this potential, many companies rely on experienced AI developers to define the most relevant use cases, align them with business priorities, and implement solutions that minimize cyber exposure while strengthening the organization’s overall security posture.
Key AI use cases in cybersecurity
Vulnerability management
Identifying and addressing potential security vulnerabilities in an organization's IT infrastructure is the first step in minimizing the risk of successful cyberattacks. AI technologies can be used to enhance key vulnerability management tools and techniques, including:
- Code review
Automatically identifying programming errors and inconsistencies that can cause security issues and suggesting code fixes. - Red teaming
Simulating cyberattacks against an organization (for instance, by creating phishing emails with generative AI) to test its security posture. - Risk scoring
Ranking critical vulnerabilities and defining patching priority based on the number of assets affected, their connection to other assets, and many other contextual variables that non-AI systems struggle to factor in. - Software patching
Generating software patches with GenAI tools and distributing them via automated scripts to speed up vulnerability remediation.
Threat detection
While traditional cybersecurity tools follow predefined rules to detect potential threats, such as a number of failed login attempts exceeding a threshold, AI systems use machine learning algorithms to autonomously identify patterns and anomalies associated with an attack. This flexible approach, which helps address complex or new scenarios not covered by rigid rules, can be applied to different types of security threats:
- Phishing attacks
Detecting suspicious communications (urgent calls to action, misspelled domains that mimic legitimate ones, etc.) which can be signs of deceptive emails and other social engineering attempts. - Malware
Identifying unusual keylogging activity, likely caused by malicious software (trojans, ransomware, etc.) attempting to acquire users’ credentials and gain unauthorized access to their accounts. - Insider threats
Spotting a surge in network traffic, which can indicate that a malicious employee is downloading large volumes of sensitive data from corporate systems. - Zero-day exploits
Proactively searching for anomalous user behaviors such as logging in from unusual locations (so-called behavioral threat hunting) to identify threats exploiting unknown and therefore unpatched software vulnerabilities.
Automated incident response
With their combination of advanced decision-making and automation capabilities, AI-powered cybersecurity tools can analyze security incidents and choose the best course of action to address them with minimal human intervention. This process typically includes:
- Incident analysis
Gathering data from multiple sources, including firewall logs and endpoint telemetry, to monitor incident progression in real time and assess their nature and potential impact. - Deduplication & filtering
Eliminating redundant alerts on the same incident from different sources and filtering false positives from actual incidents. - Triage
Defining incident severity and urgency based on contextual data (for instance, the criticality of affected assets) to prioritize those requiring immediate attention. - Containment & eradication
Triggering suitable containment measures (such as revoking compromised credentials or isolating affected endpoints) and proceeding with threat eradication (for instance, by removing the malware).
Cyber threat intelligence
In an ever-evolving field like cybersecurity, gathering information on existing or emerging types of threats, attack vectors, and malicious actors and keeping up with new cybercrime trends enables organizations to set up protection mechanisms for a more robust security posture. Cyber threat intelligence embraces this proactive approach and AI tools powered by natural language processing and GenAI can help in this regard by:
- Searching for conversations about the threat landscape
(including emerging threats such as new malware strains) on news and social media, IT forums, or even dark web sources to extract actionable insights. - Generating threat intelligence reports
in human-readable language to share the sensitive information collected with security teams and other stakeholders.
100%
Image title: Google Threat Intelligence’s report on active threat campaigns Image source: Google Cloud
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Statistics on AI in cybersecurity
Market & adoption
| The AI in cybersecurity market was valued at $24.3 billion in 2023 and is expected to reach $134 billion by 2030 | |
|---|---|
| 43% of organizations currently use artificial intelligence as part of their cybersecurity strategies | |
| 64% of cybersecurity professionals surveyed are researching GenAI-powered security tools or have already purchased one. 76% of respondents are opting for GenAI tools specifically designed for cybersecurity rather than domain-agnostic solutions | |
| 80% of security specialists prefer GenAI delivered through a comprehensive platform rather than more specialized point solutions. Specifically, these experts believe that platform-based options lead to faster returns, reduced security incidents, fewer training cycles, and lower maintenance costs |
Areas of application
Anomaly detection systems
Malware detection
Automated incident response
Alert enrichment
Predictive threat intelligence
AI-enhanced network security
User entity behavior analytics
Event or forensic investigation
Red team activities
Others
Scheme title: Most popular use cases of AI in cybersecurity
Data source: Statista
Scheme title: Cybersecurity areas where defensive AI is expected to impact the most
Data source: Darktrace
| The top cybersecurity use cases where organizations plan to use GenAI include rule creation (mentioned by 21% of respondents), cyberattack simulation (19%), compliance violation monitoring, and network detection (16%) |
|---|
Implementation benefits
| 95% of security teams surveyed believe AI can improve the speed and efficiency of cyber defense, and 88% of teams are already seeing significant time savings from AI adoption | |
|---|---|
| 71% of organizations report higher job satisfaction among their cybersecurity specialists due to AI automating tedious security tasks | |
| 58% of cybersecurity professionals think that artificial intelligence will enhance or support them, while only 12% expect it to completely replace their role | |
| 63% of cybersecurity specialists expect AI to help strengthen security measures, especially in terms of threat detection and response capabilities | |
| According to cybersecurity leaders, the security market segments that will benefit the most from GenAI include cloud security (mentioned by 55% of respondents), security operations and management (52%), and endpoint security (52%) |
Implementation concerns
Top concerns about AI adoption in cybersecurity among specialists
Data source: CSA
| 51% of stakeholders in very large organizations (25,000+ employees) expressed a lack of confidence in AI-powered cybersecurity solutions | |
|---|---|
| 18% of security leaders believe that AI devices currently offer less value than any other security solution when taking into account initial investment, time, and operational costs | |
| 56% of organizations surveyed reported that their AI solutions were unable to effectively identify new threats due to poor training data quality |
Benefits of AI-driven cybersecurity
AI-based security systems prove more effective than traditional solutions thanks to their specific approach to threat detection and response.
Superior adaptability
Unlike traditional tools that use rigid, manually compiled rules based on past security events, AI-enabled systems continuously learn patterns and anomalies from new event data, incorporating this knowledge into their models to adapt to evolving threats.
Effective threat prevention
The adoption of AI enables a more proactive approach to cybersecurity, as adopters can gather information on the most common threat scenarios to increase their readiness or identify vulnerabilities in IT systems to reduce their cyber exposure with preventative measures.
Higher accuracy
AI’s ability to identify nuanced patterns and relationships across a wide range of variables enables AI systems to achieve lower false positive and negative rates than rule-based security tools that rely on a less flexible "if/then" logic.
Security workload mitigation
Since AI-powered cybersecurity solutions don’t need precompiled rules to operate and typically generate less false positives than traditional systems, cybersecurity teams won’t be overwhelmed by time-consuming rule updates or manual reviews to verify false alarms.
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Guidelines for using AI in cybersecurity
While the use of AI in the cybersecurity sector is on the rise, many companies struggle to fully seize the benefits of this technology. Here are some key aspects to consider.
Train your AI system with high-quality data
The AI model powering a security system requires vast amounts of data to learn the patterns or anomalies recorded during security events and recognize them once they recur in future attacks. Therefore, you need to make sure these large training datasets contain relevant and accurate data. For example, training your AI system on properly labeled data points from thousands of failed login attempts will help the solution recognize unauthorized login attempts. That said, this process can be very demanding in terms of training time, IT infrastructure, expertise, and upfront investment.
Implement a governance framework
In most cases, adopting AI algorithms requires adjustments to a company’s workflows and cybersecurity practices. To facilitate this transition, create a dedicated governance framework establishing the roles of cybersecurity professionals, continuous AI model output supervision and fine-tuning via retraining iterations, routine risk assessment, and a backup plan in case of AI system failure.
Our artificial intelligence services
Itransition’s team provides AI services and solutions to help organizations strengthen their cybersecurity capabilities and minimize business risk.
AI development
Our team builds ML solutions that balance high performance with usability, handling everything from data preparation and model training to front- and back-end development, system integration, and post-launch support.
AI consulting
We provide expert support at every stage of the ML implementation journey, from business needs assessment and solution design to project planning, oversight, and user onboarding, ensuring the final system aligns fully with your expectations.
Competing with hackers in the AI field
The adoption of artificial intelligence is not the savior of legitimate organizations, but a full-blown arms race between companies and cybercriminals. While hackers are actively looking to exploit the latest technological advancements for malicious purposes (think password-guessing AI), businesses should be ready to use those same tools to improve their own cyber readiness.
If you're looking for a robust AI solution to withstand even the most sophisticated cyber threats, consider building one tailored to your needs with Itransition's expert guidance.
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