Top 13 AI Cybersecurity Use Cases with Real Examples
We present the major AI cybersecurity use cases, each followed by a real-world example demonstrating its impact. Given the widespread adoption of AI and cybersecurity solutions across diverse industries and regions, we aim to provide examples that reflect this global and cross-sector relevance.
See the table below for a summary, keep on reading for a detailed explanation of each, or jump to the most recent developments in the AI-powered cybersecurity section. You can also read how AI is transforming cybersecurity to understand the market size, key technologies, and challenges.
AI in behavioral threat detection
1. AI-powered threat detection and anomaly monitoring
AI enhances threat detection by continuously monitoring networks, endpoints, and user behaviors to identify anomalies that could indicate cyber intrusions. Machine learning models learn the environment’s “normal” behavior and flag deviations in real time. This approach helps security teams detect stealthy attacks (like malware beaconing or unusual data transfers) much faster than traditional rule-based systems.
The following is a concise summary of the evolution of threat detection:
Real-life example: Darktrace ActiveAI & Aviso
Aviso, a Canadian wealth services firm managing over $140B in assets, adopted Darktrace’s ActiveAI Security Platform to enhance cybersecurity and reduce analyst workload. The system’s self-learning AI enables automated detection and response across on-prem and cloud environments.
Darktrace claims that it generated 73 actionable alerts and autonomously investigated 23 million events. Its anomaly-based detection identified threats by spotting deviations from normal behavior, rather than relying on static rules.
Additionally, the platform reportedly strengthened email defenses, blocking over 18,000 malicious emails missed by legacy filters. These improvements enabled Aviso’s security team to focus on strategic areas, such as vulnerability management and compliance.1
2. AI for malware detection and prevention
AI is transforming malware defense by powering antivirus and endpoint security solutions. Instead of relying solely on known virus signatures (which only catch previously seen malware), AI-based tools use machine learning to identify malicious files or behaviors.
They analyze attributes of executables, memory usage patterns, or process behaviors to predict if something is malware, even if it’s a new, unknown variant. This proactive approach helps prevent zero-day attacks and polymorphic malware that traditional antivirus software might overlook.
Real-life example: CordenPharma & self-learning AI
CordenPharma, a global pharmaceutical manufacturer, aimed to protect sensitive IP and patient data with limited cybersecurity resources. Facing supply chain attacks and stealthy malware, traditional tools generated false positives and missed subtle threats.
To address this, CordenPharma implemented a tool with a self-learning AI, which established a dynamic behavioral baseline across users and devices. During the proof-of-value phase, the tool identified a crypto-mining malware infection involving beaconing to a Hong Kong endpoint and a suspicious executable download. Though operating passively, the tools’ Autonomous Response module recommended actions to block over 1GB of attempted data exfiltration 2
3. AI for account takeover and identity protection
AI plays a key role in defending against account takeover (ATO) attacks and strengthening identity and access management. By monitoring user login behaviors, device characteristics, and access patterns, AI models can spot when someone’s account is being accessed illegitimately.
Unusual login times, a surge in failed password attempts, or a new device accessing an account can all trigger AI alerts. The system might then enforce step-up authentication (like requiring multi-factor authentication) or block the login to prevent unauthorized access.
Real-life example: Memcyco & account takeover (ATO) prevention
A case study involving an AI start-up, Memcyco, involved ATO. The company claims that a global bank faced a surge in ATO incidents driven by advanced phishing campaigns. The study claims that the attacks resulted in approximately 18,500 ATO cases per year, each costing around $1,500 in remediation, totaling an estimated $27.75 million annually. Detection was delayed, often depending on customer complaints or social media exposure, and stolen credentials remained a threat even after takedown actions.
To address this, the bank deployed Memcyco’s real-time platform, which identified phishing sites at the moment of attack, alerted users, and replaced compromised data with decoys. The study claims that the system blocked attackers from using stolen credentials. The result was a 65% reduction in ATO incidents while improving detection speed and easing the workload on security teams.3
4. AI-powered insider threat detection
Not all threats originate from the outside; malicious insiders or compromised internal accounts can pose equally significant dangers. AI-based user and entity behavior analytics (UEBA) systems help detect insider threats by establishing a baseline of normal employee behavior and then alerting on abnormal activities.
This might include unusual file access patterns, large data downloads, accessing systems that are rarely used, or escalating privileges without justification. AI can sift through vast amounts of log data across the organization to pinpoint these subtle warning signs that a user account may be misused or a trusted insider might be exfiltrating data.
Real-life example: UEBA-powered SIEM platform and Golomt Bank
Using cloud-native Securonix SIEM with built‑in UEBA analytics, Golomt Bank replaced a traditional rule-based SIEM with a behavior‑based monitoring platform. This shift provided the security team with unified visibility across on-premises, cloud, and hybrid environments, as well as powerful context on user behavior.4
Securonix’s claim of impact:
- False positives were reduced by approximately 60%, resulting in less noise and improved alert quality.
- Incident investigation times improved by 40%, enabling faster triage and response.
- Analysts observed a reduction from ~1,500 raw alerts per day to under 200 vetted events, enabling a focus on genuine threats rather than duplicated or benign activity.
5. AI in IoT and OT security
The Internet of Things (IoT) and Operational Technology (OT) environments (like industrial control systems, smart factories, and utilities) present unique security challenges. These systems encompass a diverse array of devices and sensors with complex or non-standard traffic patterns, and many operate legacy or unpatched software.
AI is increasingly used to monitor IoT/OT networks, learning the regular communications between devices, and detecting anomalies that could signal a cyberattack or malfunction. This is crucial for preventing disruptions, since attacks on IoT/OT can cause physical damage or downtime (for example, altering sensor readings or hijacking industrial controllers).
Real-life example: Smart-city deployment and AI-enhanced IoT sensors
A smart-city deployment leveraged AI-enhanced IoT sensors and edge computing to monitor public safety systems, including transit networks, surveillance cameras, environmental sensors, and street infrastructure 5
Key metrics & capabilities:
- Detection accuracy: A hybrid deep-learning framework combining autoencoders, LSTM, and CNN models achieved approximately 97.5% precision and 96.2% recall, with an F1 score near 96.8%, significantly outperforming traditional IDS systems.
- Decentralized monitoring: The platform utilizes edge-based AI and federated learning, enabling real-time anomaly detection across distributed sensors while preserving data privacy and minimizing central bandwidth requirements.
- Operational resilience: In one academic prototype network (Heimdall), AI-powered lamppost-mounted cameras and sensors autonomously detected traffic anomalies and safety events with minimal human oversight.6
These implementations demonstrate how AI-driven anomaly detection across decentralized IoT/OT environments can significantly enhance citywide safety and operational uptime, without overwhelming centralized systems or violating privacy.
SOC efficiency and response automation
6. AI-driven incident response and SOC automation
Security Operations Centers (SOCs) receive an overwhelming number of alerts daily, many of which are false positives. AI assists by automating incident response workflows and prioritizing genuine threats.
In practice, AI-driven SOAR (Security Orchestration, Automation, and Response) platforms can automatically triage alerts, correlate data from multiple sources (like firewall logs, endpoint alerts, and threat intel feeds), and even execute predefined response actions (such as isolating a machine or blocking an IP) without waiting for human intervention. This reduces response times and analyst fatigue.
Real-life examples: DXC technology and AI-enabled SOC
DXC Technology implemented an AI-enabled SOC combining AI analytics with SOAR. The integration resulted in a 60% reduction in alert fatigue and reduced incident response times by approximately 50%. Automated triage and containment actions, such as isolating endpoints and blocking malicious IPs, reduced reliance on manual intervention and allowed analysts to focus on higher-priority investigations. The approach improved operational efficiency without requiring a proportional increase in security staff. 7
7. AI in alert overload
Security teams often face thousands of alerts per day from various cybersecurity tools like SIEMs, firewalls, and endpoint protection platforms. Many of these alerts are false positives or low-priority, leading to alert fatigue, delayed responses, and missed real threats.
AI helps mitigate alert overload by:
- Filtering and deduplicating alerts to reduce noise
- Correlating related alerts across systems to identify actual incidents
- Prioritizing alerts based on risk, context, and historical patterns
- Recommending or initiating automated responses to low-risk threats
Real-life example: IBM Security QRadar SIEM implementation for a Gulf-based Bank
A notable example of AI addressing alert overload in cybersecurity is the deployment of IBM Security QRadar SIEM at a central bank in the Gulf region. This institution faced significant challenges with its previous Security Information and Event Management (SIEM) system. The legacy system generated an overwhelming number of alerts, many of which were false positives, leading to alert fatigue among security analysts and delayed response times.
To overcome these challenges, the bank implemented IBM Security QRadar SIEM, leveraging its advanced AI and machine learning capabilities. The implementation of IBM Security QRadar SIEM resulted in a reduction in alert volume and false positives. 8
Threat intelligence and proactive defense
8.AI in threat intelligence and predictive defense
Beyond reacting to known threats, AI is used for predictive threat intelligence, analyzing massive amounts of security data to forecast attacks and uncover emerging threats. AI models can ingest unstructured data from security blogs, hacker forums, malware telemetry, and incident reports worldwide to identify patterns or signals that may indicate a new campaign or technique is emerging.
This helps organizations move toward a proactive defense, such as strengthening specific systems or alerting industries about a threat before it fully materializes. AI also aids threat hunters by highlighting suspicious patterns in logs that warrant human investigation, effectively guiding experts to lurking threats that have not yet triggered any alarms.
Real-life example: IBM’s Watson AI
IBM’s Watson AI has been used for predictive threat intelligence by analyzing research papers and threat feeds to identify malware behavior patterns, allowing for earlier detection of emerging campaigns. Similarly, AI-driven analytics have flagged insider threats by monitoring deviations in communication and data access. In one case, an employee’s unusual access to HR and confidential files prompted a timely intervention before harm occurred.
In another instance, AI detected an increase in dark web activity related to a specific software vulnerability, prompting preemptive patching before attackers could exploit it. These cases highlight how AI enables proactive cybersecurity by anticipating threats rather than reacting to them.
Communication & content threat detection
9. AI in email security and phishing prevention
Phishing emails and malicious spam are a top entry point for breaches. AI bolsters email security by analyzing message content, context, sender history, and even writing style to detect phishing attempts that evade basic filters. Machine learning models can flag subtle signs of spear-phishing, such as an email that mimics a CEO’s tone or unusual phrasing that indicates a spoofed message. AI can also scan attachments and URLs for suspicious characteristics, helping to prevent malware or credential theft via email.
Real-life example: Google’s ML & phishing detection
Google’s Gmail service processes emails for over 1.5 billion users worldwide. The platform faces millions of phishing attempts daily, where attackers send emails that appear legitimate but are designed to steal sensitive information from users.
Google uses machine learning models to enhance its phishing detection capabilities. These models are trained on datasets of email characteristics, including the email’s content, sender information, and metadata. 9
10. AI for content moderation and threat detection in social media
While it could be argued that content moderation is not directly related to cybersecurity, it involves aspects of cybersecurity practices, including detecting and mitigating threats, ensuring real-time responses, and protecting user rights. These platforms must identify and minimize harmful content, including misinformation, hate speech, and illegal activities, while striking a balance between user privacy and freedom of expression.
Malicious actors often use sophisticated techniques to evade detection. The cybersecurity challenge here is to stay ahead of these evasion techniques to prevent harmful content from spreading.
Real-life example: Facebook social media monitoring for threat detection
Facebook uses Natural Language Processing (NLP) to scan and analyze posts, comments, and messages for harmful content. NLP algorithms detect keywords and phrases associated with threats and extremist content, while sentiment analysis gauges the emotional tone, flagging posts with extreme negativity or aggression.
Advanced models identify and then analyze data and the context around specific entities to understand broader implications. Topic modeling groups similar posts to identify trends or coordinated campaigns. The system recognizes deviations in language and behavior patterns, signaling potential threats. Continuous updates and machine learning on large datasets of harmful content enhance detection accuracy and reduce the number of false positives. 10
Financial risk mitigation
11. AI for financial fraud detection
In banking and online payments, AI is used to detect fraud by monitoring transaction patterns and user behavior in real-time. Machine learning models analyze factors like spending habits, login locations, device identifiers, and transaction anomalies to flag potentially fraudulent activities (e.g., credit card theft, account takeover, money laundering). Unlike static rules, AI systems continuously adapt to new fraud tactics and can catch subtle irregularities across millions of transactions.
Real-life example: Visa & AI-driven fraud detection
In 2023, Visa leveraged AI to prevent 80 million fraudulent transactions totaling $40 billion. Its AI models assess transaction risk in milliseconds, detecting anomalies such as unusual charges or spending patterns before they impact customers.
Major banks utilize similar AI systems to monitor logins and fund transfers, flagging events such as large transactions from unfamiliar locations or devices. These tools often enable real-time fraud prevention by freezing suspicious activity and notifying analysts.
Visa’s case highlights how AI has become a central component in fraud mitigation across the financial sector. 11
12. AI for financial data discovery and protection
AI enhances financial data security by automatically discovering, classifying, and protecting sensitive data such as bank account numbers, credit card records, and transaction logs. With machine learning, organizations can scan massive datasets to identify high-risk data, detect access anomalies, and ensure compliance with regulations like PCI DSS and GDPR.
Real-Life Example: Capital One’s Cloud Transformation & AWS
Capital One adopted AI-powered security tools as part of its cloud transformation. It deployed AWS Macie, a machine learning–based solution that continuously scans data to detect and classify sensitive financial records.
Additionally, Capital One has integrated automated threat detection and response systems to monitor data access patterns and respond to suspicious activity in real-time. When anomalies were detected, such as unauthorized access or abnormal data movement, the AI systems could automatically isolate compromised systems and alert security teams. This significantly reduced the time to contain threats and minimized potential data leaks.
Vulnerability and patch management
13. AI for vulnerability management and patch prioritization
Large organizations must manage thousands of software vulnerabilities and misconfigurations. AI assists in vulnerability scanning and management by automating the scanning of systems and applications for known weaknesses and by prioritizing which vulnerabilities to fix first.
Machine learning models can assess not only a vulnerability’s severity score, but also contextual risk factors, such as whether there are active exploits in the wild, whether the vulnerable system is business-critical, or if an attacker is likely to target it. AI can even predict which vulnerabilities are likely to be exploited soon based on trends, helping teams remediate proactively.
Additionally, AI can streamline patch management by recommending or even automatically applying patches and configurations in a safe and ordered manner.
Real-life example: U.S government agency and tenable
A U.S. state government agency modernized its cybersecurity with Tenable.sc Continuous View to address phishing threats, regulatory mandates (IRS 1075/NIST 800‑53), and a distributed workforce. The shift enabled automated, real-time vulnerability management and prioritization of patches.
Tenable.sc provided continuous visibility across all device types. Its AI-driven analysis highlighted high-risk systems, such as endpoints with outdated antivirus software. The claimed key outcomes included:12
- 90% drop in phishing incidents (from 10–15 daily to near zero)
- 50% reduction in security staff workload, enabling focus on strategic tasks
- Automated patching and real-time alerts for at-risk assets
- Simplified compliance reporting via prebuilt dashboards
- Improved uptime and reduced user disruption
The most recent developments in AI-powered cybersecurity
The cybersecurity landscape is continually evolving, and AI is at the forefront of both new defenses and new threats.
Generative AI for offense
Cybercriminals are increasingly using generative AI to enhance their attacks. Research has found that hackers are leveraging AI coding assistants to create malware, including a new remote access trojan (RAT), at a faster rate than ever. This lowers the skill barrier for developing advanced threats. AI is also being used to craft more convincing phishing messages, fake websites, and even open-source software packages with hidden backdoors.13
Deepfake scams and impersonation
The rise of AI-generated deepfakes (ultra-realistic fake audio or video) has introduced a new kind of cyber fraud. A notorious 2019 case in the UK was the first known instance of a deepfake voice being used in a scam call – criminals synthesized the voice of a CEO to trick an employee into wiring about $243,000 to the attackers’ account. The fake voice perfectly mimicked the CEO’s accent and speaking cadence, convincing the victim of its authenticity. Since then, reports of such “voice phishing” scams have grown.
Large language models in defense
The cybersecurity industry is increasingly adopting generative AI for defense. Microsoft’s Security Copilot, launched in 2023, is the first AI co-pilot for security teams, built on OpenAI’s GPT models and Microsoft’s threat intelligence. It helps analysts summarize incidents, detect patterns, and generate scripts using natural language. In Microsoft’s trials, it reduced investigation and reporting times by up to 90%. 14
Tasks such as writing incident summaries or sifting through logs can now be automated, enabling junior analysts to work like seasoned veterans. Other companies are also integrating AI into tools for secure code review, cloud configuration, and user behavior analysis, marking a broad shift toward AI-powered cyber defense to keep pace with AI-driven threats.
Discover more on LLMs in cybersecurity.
AI Governance and Collaboration
With AI’s expanded role, there’s also a push for governance, transparency, and collaboration in the cybersecurity community. Agencies like the U.S. Cybersecurity and Infrastructure Security Agency (CISA) have started publishing AI use case inventories to be transparent about how they deploy AI for national cyber defense (for instance, using NLP to scrub sensitive data from threat reports, or scoring threat indicators by confidence using ML).
On the industry side, cybersecurity vendors are increasingly sharing AI models and threat data to enhance collective defenses. Open-source projects are emerging for applications such as AI-powered intrusion detection and malware analysis, allowing smaller organizations to benefit from AI without having to build everything from scratch. Meanwhile, the focus on AI ethics and reducing false positives remains high. Security teams recognize that AI is not infallible and are developing “explainable AI” techniques to enable analysts to understand and trust AI decisions.
Explore more on AI governance efforts, responsible AI principles and platforms, AI bias, and AI ethics.
Reference Links
Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE and NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and resources that referenced AIMultiple.
Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised enterprises on their technology decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.
He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.
Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.
Be the first to comment
Your email address will not be published. All fields are required.