Top 10: AI Tools for Enhancing Cybersecurity

It's a well-worn cliché, but cybersecurity has never been more important. It's a tautology: as our world becomes more and more digitalised, the number of targets for cyberattacks increases.
This can be seen through the proliferation of remote work, cloud computing and interconnected devices, all of which are often under threat from increasingly sophisticated cyberattacks.
Businesses and individuals alike face threats ranging from ransomware to fraud, from data breaches to advanced persistent threats.
The stakes are higher than ever before; a single breach can result in devastating financial losses, reputational damage and legal consequences.
Despite this, AI is proving to be a real game changer in the fight against cybercrime.
By using machine learning, deep learning and automation, AI tools can help users to detect, prevent and respond to threats faster and more effectively than traditional methods.
There are AI tools that can provide real-time threat detection, prevent instances of fraud and monitor insider threats.
All in all, AI tools are quickly becoming indispensable to businesses that want to secure their assets, protect their customers' data and maintain trust in an increasingly digital world.
This week, we at Cyber Magazine are shining a light on 10 of the best AI cybersecurity tool in the game today, looking through their applications and histories.
10. CyberX (now part of Microsoft)
Created in: 2013
Based in: Waltham, Massachusetts, USA
CEO: Nir Giller and Omer Schneider (Co-founders)
Main applications: Industrial cybersecurity, anomaly detection, and critical infrastructure protection
CyberX specialises in securing industrial control systems (ICS) and critical infrastructure.
Its AI-powered anomaly detection monitors network traffic for suspicious activity that could signal cyberattacks on industrial equipment.
Now part of Microsoft, CyberX combines robust industrial expertise with advanced AI, providing unmatched security for critical operational environments.
9. Senseon
Created in: 2017
Based in: London, United Kingdom
CEO: David Atkinson
Main applications: Threat triaging, automated response, and security optimisation
Senseon acts as an AI-powered security analyst, correlating alerts from multiple sources to triage and prioritise genuine threats.
Its autonomous response capabilities save security teams valuable time by neutralising certain attack types without manual intervention.
Senseon’s ability to reduce alert fatigue while enhancing efficiency makes it an indispensable cybersecurity ally.
The platform’s adaptive learning enhances detection accuracy over time.
8. Tessian
Created in: 2013
Based in: London, United Kingdom
CEO: Tim Sadler
Main applications: Email security, human error prevention and data loss prevention
Tessian addresses security vulnerabilities caused by human error. Its AI learns email communication patterns, flagging anomalies such as misdirected emails or potential data exfiltration.
By preventing accidental data loss or unauthorized sharing, Tessian safeguards sensitive information, empowering organisations to tackle human-layer security challenges effectively.
Tessian’s user-friendly interface ensures easy adoption across various organisational levels and prices start at around US$5 per user, per month.
7. SparkCognition
Created in: 2013
Based in: Austin, Texas, USA
CEO: Amir Husain
Main applications: Threat analytics, predictive security and unstructured data analysis
SparkCognition harnesses AI to predict and prevent cyberattacks by analysing vast datasets for hidden patterns.
Its platform also processes unstructured data, such as news articles and security reports, to deliver actionable threat intelligence.
With its cognitive analytics capabilities, SparkCognition provides organisations with the tools to anticipate and mitigate potential threats proactively.
Its ability to operate across diverse data sources ensures comprehensive protection against multifaceted threats.
6. LogRhythm
Created in: 2003
Based in: Boulder, Colorado, USA
CEO: Chris O'Malley
Main applications: SIEM, UEBA and compliance automation
LogRhythm integrates AI into its SIEM platform for advanced threat detection and compliance. With User and Entity Behavior Analytics (UEBA), it identifies unusual activities that might indicate insider threats or account compromises.
Its ability to streamline incident responses and provide actionable insights empowers security teams to act decisively, making it a critical asset for maintaining security intelligence.
LogRhythm also supports extensive compliance reporting, simplifying audits for regulated industries. For small deployments, prices can start at around US$20,000 per year.
In 2024, LogRhythm merged with its rival Exabeam, with Exabeam's CEO Chris O'Malley taking charge of the new conglomerate.
5. Sift Science
Created in: 2011
Based in: San Francisco, California, USA
CEO: Kris Nagel
Main applications: Fraud prevention, risk decisioning and account security
Sift’s AI-powered fraud platform uses advanced ML learning, leveraging a data network scoring 1 trillion events annually, to deliver real-time fraud detection.
Sift’s solutions protect the entire customer journey—from account creation to payments and chargebacks—while ensuring smooth consumer experiences.
Businesses can benefit from Sift's tailored and automated risk decisioning capabilities, significantly reducing losses and enhancing customer trust.
4. Deep Instinct
Created in: 2015
Based in: New York City, USA
CEO: Guy Caspi
Main applications: Endpoint security, cross-platform protection and zero-day threat prevention
Deep Instinct pioneers a prevention-first cybersecurity model, powered by deep learning.
This approach enables it to predict and prevent known, unknown, and zero-day threats across endpoints, servers, mobile devices and the cloud.
Its cutting-edge technology ensures comprehensive protection while minimising the strain on IT teams, making it ideal for organisations seeking robust cybersecurity solutions.
Deep Instinct also boasts an impressive speed of threat detection, ensuring real-time defence against emerging attacks.
According to some sources online, Deep Instinct's services are available at between US$50 - US$75 per endpoint annually.
3. Vectra AI
Created in: 2012
Based in: San Jose, California, USA
CEO: Hitesh Sheth
Main applications: Threat detection, network traffic analysis and risk prioritisation
Vectra AI specialises in identifying active cyberattacks by analysing network traffic for attacker behaviours rather than relying on known malware signatures.
Its flagship product, Vectra Cognito, offers unparalleled attack visibility and prioritises threats based on risk level, enabling faster and more effective incident responses.
This focus on proactive threat hunting makes it a trusted tool for network security and means that security teams can spend less time on investigating false positives.
2. Microsoft Security Copilot
Created in: 2023
Based in: Redmond, Washington, USA
CEO: Satya Nadella
Main applications: Threat intelligence, incident response and cybersecurity automation
Microsoft Security Copilot integrates the power of AI with Microsoft’s extensive cybersecurity ecosystem to deliver advanced threat intelligence and automation.
Designed to assist security teams, it accelerates threat detection and response by analysing vast amounts of data and generating actionable insights.
Copilot’s natural language processing capabilities allow security analysts to interact with it intuitively, asking questions and receiving detailed responses.
By automating repetitive tasks and providing contextual recommendations, Copilot can enhance efficiency and reduce burnout in corporate security teams.
This cutting-edge tool also integrates seamlessly with Microsoft’s Azure and Defender platforms, offering unparalleled visibility and control.
According to CEO Satya Nadella, the amount of people using Microsoft Copilot has more than doubled quarter over quarter since its inception.
In terms of costs, Microsoft Security Copilot is priced based on the number of Security Compute Units (SCUs) used per hour, at US$4 per SCU.
1. Darktrace
Created in: 2013
Based in: Cambridge, United Kingdom
CEO: Jill Popelka
Main applications: Threat detection, autonomous response and network monitoring
Darktrace employs self-learning AI to establish a “pattern of life” for every device and user in a network.
By continuously learning and adapting, it detects subtle deviations that signal emerging threats, including unknown or zero-day attacks.
Darktrace Antigena goes further, offering real-time autonomous threat response to contain in-progress attacks, mitigating damage before human intervention is ever needed.
This capability makes it a standout solution in proactive cybersecurity. It's usability is also highly regarded, with users saying Darktrace's intuitive dashboard allows security teams to visualise and manage threats more easily.
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