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Artificial intelligence in cybersecurity:
usage scenarios, trends & best practices

September 25, 2025

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.
Cyber threat intelligence image

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

Statista

43% of organizations currently use artificial intelligence as part of their cybersecurity strategies

SANS

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

CrowdStrike

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

CrowdStrike

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%)

CSA

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

Darktrace

71% of organizations report higher job satisfaction among their cybersecurity specialists due to AI automating tedious security tasks

SANS

58% of cybersecurity professionals think that artificial intelligence will enhance or support them, while only 12% expect it to completely replace their role

CSA

63% of cybersecurity specialists expect AI to help strengthen security measures, especially in terms of threat detection and response capabilities

CSA

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%)

McKinsey

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

Darktrace

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

Arctic Wolf

56% of organizations surveyed reported that their AI solutions were unable to effectively identify new threats due to poor training data quality

SANS

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.

Itransition’s team provides AI services and solutions to help organizations strengthen their cybersecurity capabilities and minimize business risk.

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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.

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