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

October 27, 2026
Aleksandr Ahramovich

by Aleksandr Ahramovich , 

Head of AI/ML Center of Excellence

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.

Cyber security

Key AI use cases in cybersecurity

AI software can enhance organizational resilience by streamlining diverse defensive workflows, from vulnerability management to incident response, enabling early risk identification and reducing manual effort.

Vulnerability management

AI-powered solutions automate the identification and remediation of security gaps in IT infrastructure. By using machine learning algorithms, organizations can predict which vulnerabilities are most likely to be exploited, allowing teams to prioritize patches before an attack occurs. 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

AI systems outperform traditional rule-based tools by using machine learning for anomaly detection. Such solutions come with the following capabilities:

  • Autonomous pattern recognition
    Processing millions of events to flag deviations from normal user and device activity that indicate a potential attack.
  • Detection of unknown “zero-day” threats
    Proactively searching for anomalous user behaviors such as logging in from unusual locations to identify threats exploiting unknown and therefore unpatched software vulnerabilities.
  • Identification of phishing attacks
    Scanning incoming emails and messages for urgent language, spoofed domains, and unusual sender behavior and analyzing the context of the communication to catch sophisticated social engineering attempts that bypass traditional security tools.
  • Automated rule updates
    Modifying threat detection logic based on real-time threat intelligence and newly discovered attack patterns.

Automated incident response

AI software automates incident response by analyzing telemetry data in real time to neutralize threats. This reduces human error and ensures that critical actions, like revoking compromised credentials, happen instantly to prevent data exfiltration. This process typically includes:

  • Incident analysis
    Gathering data from multiple sources, including firewall logs and endpoint telemetry, to monitor network traffic and 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 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 deepfakes and generative AI-powered malware) 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.
Google Threat Intelligence dashboard

Image title: Google Threat Intelligence’s report on active threat campaigns
Image source: Google Cloud

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Statistics on AI in cybersecurity

Organizations across industries use AI for phishing detection, intrusion response, and security workflow automation, improving security specialists’ productivity and reducing costs. However, as AI adoption in cybersecurity grows, organizations face AI implementation hurdles, such as skills shortages, budget constraints, and governance concerns, that need to be addressed.

Market & adoption

About 23% of state chief information security officers (CISOs) have already employed generative artificial intelligence (GenAI) to enhance cybersecurity operations, with 21% planning to implement GenAI within 12 months

Statista

Currently, 77% of security stacks employ generative AI

Darktrace

Worldwide AI in cybersecurity spending reached $51.3 billion in 2026 and is expected to rise to $86 billion in 2027

Gartner

94% of security leaders believe AI will be the driving force behind cybersecurity transformation in the coming year

WEF

The AI in cybersecurity market is projected to expand at a compound annual growth rate (CAGR) of 14.8% from 2026 to 2031, reaching $50.83 billion by 2031

MarketsandMarkets

AI use in cybersecurity increased from 50% to 78% in one year

SANS Institute

Areas of application

57% of cybersecurity leaders identified fraud prevention as the most promising AI application in cybersecurity, with predictive analytics ranking second at 56%

Statista

Has your organization implemented any AI-enabled tools to fulfil its cybersecurity objectives? (select up to three)

Scheme title: AI use cases in cybersecurity
Data source: WEF

Yes, for phishing & email threat detection

Yes, to detect & respond to intrusions or anomalies

Yes, for automating security operations

Yes, for user-behaviour analytics & insider threat detection

Yes, for threat intelligence & risk prioritization

Yes, for other purposes

Scheme title: AI use cases in cybersecurity across industries
Data source: WEF

Implementation benefits

96% of cybersecurity experts agree that AI can significantly increase their productivity

Darktrace

According to 96% of security leaders, defensive AI boosts the effectiveness of their security measures

Darktrace

Extensive use of AI and automation in security allows firms to save $1.93 million per incident compared to those that do not employ them

IBM

99% of security leaders think that the strategic use of AI will transform their organization’s proactive and defensive cybersecurity strategies

EY

Implementation concerns

According to 62% of cybersecurity professionals, the over-reliance on AI advice in cybersecurity is a major concern

Statista

Scheme title: AI implementation barriers in cybersecurity
Data source: WEF

Scheme title: Responsible AI adoption barriers in cybersecurity
Data source: EY

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 fewer 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. By following best practices covering data preparation and AI governance, organizations can deploy AI securely and sustainably.

Train your AI system with high-quality data

The AI model powering a security solution requires vast amounts of training data to learn to distinguish between normal user or system behavior and suspicious or malicious activity. To ensure detection accuracy, companies need to:

  • Use large, labeled training datasets
  • Make sure that training datasets reflect real-world attack patterns
  • Invest in specialized data engineering expertise
  • Train the AI system to handle low-quality data
Implement a governance framework

In most cases, adopting AI algorithms requires adjustments to a company’s workflows and cybersecurity practices. Best practices for AI security governance include:

  • Defining clear roles for human oversight
  • Establishing continuous model retraining pipelines
  • Performing routine risk assessments of AI outputs
  • Maintaining a manual backup plan for system failures

Our artificial intelligence services

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

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AI development

Our team builds ML solutions that balance high performance and scalability with usability, handling everything from data preparation and model training to front- and back-end development, system integration, and post-launch support.

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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.

From our clients’ perspective

We are very grateful to Itransition for their contributions. We highly recommend their services to software product vendors in need of AI-related innovation and process consultancy.

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Jim Roundtree

CFO, AiBUY

Itransition was able to quickly and easily capture our requirements and create a solution that worked for us.

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Jon Burkhart

Vice President of IT, Digitech Computer LLC

Over the course of our collaboration with Itransition, we were consistently impressed with both skill and dedication their team employed to fulfil our business needs.

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Media Director, YouGov Sport

About Itransition

5+ years of experience in AI development and consulting

10+ years of DevOps and DevSecOps consulting experience

Dedicated AI/ML Center of Excellence

Holding a validated AI Platform on Microsoft Azure specialization

Long-standing partnerships with Microsoft and AWS

Established MLOps practices

Delivering software solutions fully compliant with data security standards such as PCI DSS, GDPR, CCPA, SOC 2, and IFRS

Quality and information security management systems compliant with ISO 9001 and ISO 27001

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. Hackers use AI for automated password guessing, sophisticated phishing, and misinformation generation. To stay ahead, businesses should adopt defensive AI solutions that can counter evolving threats in real time.

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.