Leveraging AI tools to enhance cybersecurity strategies has become the norm among industry leaders who value data privacy, security, and risk management. Security teams deploy AI tools across multiple cybersecurity tasks to ensure real-time threat detection and response capabilities with the added benefit of advanced threat intelligence and vulnerability management.
AI in cybersecurity was valued at $30 billion in 2024 but is estimated to reach a new value of $134 billion by 2030. A state of AI survey also found that many organizations are using AI to improve their cybersecurity strategies and reduce their risks. Discover how AI tools can enhance cybersecurity and the top 10 platforms being used to achieve the desired results without adding more risk.
How Can Artificial Intelligence Improve Cybersecurity?
AI cybersecurity tools have the potential to support understaffed security teams, implement real-time threat detection and response strategies, and manage vulnerability and risk. Here’s a quick breakdown of how AI in cybersecurity can serve businesses seeking intelligent cybersecurity strategies:
Vulnerability Management
Cybersecurity professionals use AI in cybersecurity to detect known and unknown threats in a landscape with cyber criminals constantly evolving more sophisticated threats for seamless vulnerability management. Security teams deploy user and entity behavior analytics (UEBA) to detect advanced or emerging threats and unusual behavior that may indicate zero-day attack patterns faster.
Real-Time Threat Detection and Response
Google and DeepMind AI admitted that generative AI and large language models in cybersecurity had become integral to enhanced responses at a conference last year. Artificial intelligence has the ability to use real-time threat hunting to accelerate threat detection and improve incident response capabilities for security experts as it can automatically discover activities from cyber criminals.
Efficient Access Management
AI cybersecurity tools like Accenture expanded its generative AI capabilities to offer a proactive defense against cyber criminals. Using generative AI in cybersecurity also protects sensitive data from malicious actors by implementing simple yet effective security measures that improve access controls. Supply chain companies often use effective AI systems that provide access controls and authentication.
Behavioral Analysis
Other organizations improve their threat intelligence through behavioral analytics that identifies unusual behavior to detect cyber threats earlier. Security teams deploy AI to identify emerging threats by analyzing user behavior and recognizing indicators of compromise (ICOs). Launching AI systems that scan, analyze, and detect threats based on unusual behavior patterns enhances the security strategy.
Phishing Detection and Prevention Control
AI in cybersecurity is also utilized to reduce phishing attacks, a common threat in the digital security realm. AI security can reduce the likelihood of phishing attacks by discovering anomalies and malicious actors in email threads. Identify threats to prevent and control phishing attacks from different attack vectors through generative AI and machine learning AI models that continuously scan emails.
Automate Routine Tasks
AI-powered systems can improve security operations by automating security tasks, which is particularly useful when businesses are understaffed. Leveraging AI to automate repetitive tasks related to security operations can help understaffed security teams detect potential threats quickly and reduce human error. The role of AI in detecting and mitigating human error has also become prevalent.
Improved Cybersecurity Strategies
Artificial intelligence has made quite an impact in fight against fraud, which helps high-risk businesses and security analysts ensure they implement the leading security measures to prevent fraud, money-laundering, and other nefarious actions from cyber criminals that target high-risk industries. Security analysts in healthcare, finance, and banking often use AI systems for automated fraud detection.
Insider Threat Detection
Threat hunting with AI tools isn’t always about looking outside the system. Sometimes, insider threats can pose more risk than cyber criminals. Insider threats are defined by an authorized users freely giving out access to malicious actors that pose a significant cyber risk to the company. Use AI-powered security to identify and respond to insider threats faster than traditional systems.
Enhanced Threat Intelligence
Last but not least, various organizations are using cybersecurity artificial intelligence tools to advance threat intelligence and predict zero-day attacks. Advanced threat intelligence enables human analysts to forgo the time-consuming threat detection tasks like reverse engineering and understanding complex languages by using generative AI to speed up the analysis process and incident response capabilities.
How Different Cybersecurity Artificial Intelligence Tools Work
There are many different types of AI, with some serving cybersecurity professionals well. Most security experts use some form of generative AI, but there are four types of AI that serve security teams well. Here’s a breakdown of the primary AI technologies used to improve cybersecurity:
Generative AI (Gen AI)
Understand how generative AI can be used in cybersecurity because the AI models have many different benefits for cybersecurity professionals. For example, generative AI can automatically discover emerging threats, update security patches, and detect threats in the network traffic faster than human analysts. Generative AI can also enhance the overall security posture of a company.
Machine Learning (ML)
Machine learning models can identify potentially sophisticated threats, emerging threats, and malicious code by comparing the training data that contains clean and malicious files. Machine learning models process and analyze data much faster and could reduce human error. The future of machine learning in cybersecurity has already unfolded, turning reactive into proactive defense.
Natural Language Processing (NLP)
NLP is an AI-powered technology that understands sentiment in human language, even that belonging to cyber criminals. It can improve your security strategy because it can detect threats based on sentiment, not just user behavior. NLP for cybersecurity has proven effective because it doesn’t simply detect threats. It can understand the sentiment and meaning behind every behavior and comment.
Large Language Models (LLM)
Large language models often refer to generative AI tools like Chat GPT and Gemini. Sure, LLM’s in cybersecurity carry some risks due to ethical, bias, and data privacy concerns. However, these models are capable of detecting attack patterns, malicious actors, and insider threats quickly. LLM security solutions use threat hunting and can even automate some simple security workflows.
Top 10 AI Cybersecurity Tools
AI models have transformed the protection of sensitive data, allowing security teams to detect advanced threats, respond to security incidents faster, and reduce security risks. Here’s our list of AI security tools that streamline the responsibilities of any security analyst who follows the best practices.
Microsoft Security Copilot
Microsoft Security Copilot is a widely used AI model that doubles as a virtual assistant dedicated to identifying attack patterns and reducing security incidents with minimal false positives. Microsoft Security Copilot can analyze vast amounts of data, prioritize potential threats in real-time, and recommend responses. It’s ideal for companies already using Microsoft products.
Pros:
- Seamless integration with other Microsoft products
- Extensive features and cybersecurity capabilities
- Rapid threat detection and response suggestions
- Provides valuable existing security insights
Cons:
- Can’t integrate with non-Microsoft products
- Must be customized to adapt to unique environments
Google Threat Intelligence
Google Threat Intelligence has transformed how security experts use AI in cybersecurity, detecting sophisticated threats in network traffic with real-time threat detection capabilities. The AI-driven solution provides comprehensive tools designed for cloud users who want investigations, analysis, and automated tasks. The platform also provides complete visibility on context.
Pros:
- It has comprehensive tools that improve threat-based intelligence
- Access risk management solutions with investigative features
- Automate repetitive tasks like patch updates and incident reactions
Cons:
- The integration capabilities are limited
- It doesn’t provide internal threat detectors
Sentinel One
Sentinel One is another fantastic cybersecurity platform with advanced technologies to support understaffed teams or help security professionals stay ahead of cyber threats in different attack vectors to improve the company’s overall security posture. The system combines endpoint and extended detection and response features to improve incident response capabilities.
Pros:
- Provides real-time response capabilities
- Comprehensive endpoint visibility to reduce cyber threats
- Advanced features to detect known and unknown threats
Cons:
- The tool is complex and has a steep learning curve
- The reporting tools require tons of employee training
IBM Cybersecurity Assistant
The IBM Cybersecurity Assistant is another virtual assistant that analyzes user behavior using ML models and generative AI. It also processes large amounts of data relevant to network traffic compared to traditional systems to ensure streamlined detection and response activities. The tool also uses ML capabilities to continue learning from new data and is able to respond to evolving threats.
Pros:
- Automatically discover new and evolving threats with future sight
- Investigate security incident alerts manually or automatically
- It makes response suggestions based on historical and existing data
Cons:
- The tool is advanced and may challenge new teams
- It costs a lot, even if it has extensive features
Darktrace
Darktrace is a cyber AI tool capable of detecting known and unknown threats across different attack vectors. The AI-powered incident response and threat detection tool uses a combination of generative AI and machine learning to continuously improve its approach to reducing cyber threats. It doesn’t simply scan user behavior to detect current threats. It also predicts zero-day threats.
Pros:
- Learns and evolves with your network traffic
- Can detect sophisticated threats and unknown threats
- Can automate routine tasks like patch updates and responses
Cons:
- The initial setup is complex with a steep learning curve
- It costs too much for smaller security teams
Tessian
Tessian is an AI-powered email security tool that supports human analysts to stay ahead of emerging threats and reduce the risk of phishing attacks from multiple attack vectors. The system provides AI expertise also capable of automating some simpler email security workflows. Tessian uses a combination of data scans, behavioral analysis, and threat network traffic insights.
Pros:
- Reduces the risk of phishing-style attacks
- Has built-in cybersecurity coaching for employees
- Seamless integration with Google and Microsoft environments
Cons:
- Has been known to give false positives
- Costs too much for smaller businesses
Vectra AI
Vectra AI improves threat detection and response strategies across all vectors, including identity, data, cloud, SaaS, and physical infrastructure. The AI tool has attack signal intelligence that exceed simple analysis by tracking actual attack signals in real-time. Vectra AI is famous for using attack signal intelligence combined with behavioral analysis to detect and respond to threats swiftly.
Pros:
- Uses XDR with traditional network detection and/or response for automated responses
- It prioritizes threats based on context and reduces alert fatigue
- Integrates seamlessly with multiple other cybersecurity solutions
Cons:
- It has limited reporting capabilities for efficient AI governance
- It lacks enough documentation to train new users
Cybereason
Cybereason is an excellent AI in cybersecurity system that uses generative AI-powered capabilities to detect cyber threats across all endpoints. Use security measures based on behavioral analysis, cross-machine correlation, threat hunting, and EDR or XDR threat investigations. Cybereason also enables one-click responses after making insightful recommendations based on the user behavior or network traffic.
Pros:
- Powerful against ransomware and zero-day threats
- Provides complete attack narrative views for investigations
- Eliminate cyber threats quickly with a single click
Cons:
- The platform requires a steep learning curve
- It isn’t suitable for smaller startups and teams
Kriptos AI
Kriptos AI is another customized generative AI model that combines other technologies to protect sensitive data and other digital assets to improve the company’s overall security posture. The Kriptos AI-powered platform analyzes thousands of bits of information from multiple documents to determine their confidentiality levels, with many companies leveraging AI from Kriptos to spot insider risks.
Pros:
- Provides semantic endpoint protection for all data
- Integrates seamlessly with the McAfee Security Platform
- Relies on user and entity behavioral analysis for better security measures
Cons:
- The platform is too complex for amateur cybersecurity teams
- The price is steep and appeals more to larger organizations
Secureframe Comply AI
Secureframe AI is another cybersecurity tool that uses advanced technologies like machine learning and generative AI to detect emerging threats and handle incidents based on actionable insights. The platform can even conduct automatic risk assessments to reduce the risk of cybersecurity threats from malicious attackers, making it another ideal choice for proactive threat management.
Pros:
- Integrates well with various other security technologies
- Uses cloud computing to reinforce the reduction of security incidents
- Leverages machine learning and NLP to predict emerging threats
Cons:
- The platform requires some technical knowledge
- It becomes pricy for smaller startups and teams
AI Cybersecurity Tools Conclusion
AI in cybersecurity has made extensive waves, creating opportunities for security professionals and analysts to redefine their workflows and automate repetitive tasks. That way, they can improve the overall security posture of the organization and enhance the cybersecurity protocols to reduce risks.
Generative AI plays an integral role, but so does NLP, ML, and LLM models. Some provide extensive vulnerability management capabilities while others speed up how companies detect potential cyber threats. AI cybersecurity tools empower professionals with faster and more efficient threat detection.
Cybersecurity Artificial Intelligence Tools FAQs
What are the best practices for using AI cybersecurity tools?
Anyone using AI-powered tools to reduce cyber risk and improve any specific security measure should follow the best practices to ensure compliance and the mitigation of evolving threats. Here are some cybersecurity best practices when using AI technologies:
- Implement comprehensive and responsible AI governance, visibility, and accountability.
- Integrate AI security standards, access controls, and automated reporting tools.
- Practice zero trust with AI technologies and review every decision and response.
- Conduct regular security audits and AI reviews to make sure the decisions are correct.
What are the risks of using cybersecurity artificial intelligence tools?
AI-powered systems are helping security professionals and analysts protect data when they use trustworthy AI with minimal false positives. However, some untrustworthy AI platforms with unreliable training data may also cause havoc in the world of innovation and technology. Here are some ways AI threatens cybersecurity, especially generative AI:
- Cyber attackers increasingly generate sophisticated attacks using Gen AI, with one report finding that 84% of respondents suggest it makes attacks more complex.
- The Global Security Outlook shows that there has been an increased volume of attacks due to Gen AI accessibility and widespread adoption.
- AI-driven data leaks have impacted 68% of organizations in 2024 because a lot of sensitive data is shared in generative AI responses.
- The use of AI in cybersecurity software development could result in greater vulnerabilities, according to a recent Stanford study.
What are examples of generative AI in cybersecurity?
Here are some different AI technology examples with use cases for various security measures companies can improve by using their integrated tools:
- AI-powered endpoint protection uses ML models to detect and prevent advanced threats like ransomware and malware. They analyze network traffic, user behavior, and file behavior.
- AI-powered behavioral analytics uses ML algorithms to identify unusual patterns of user behaviors across different systems, revealing insider risks and compromised authorizations.
- AI-powered intrusion detection systems use advanced ML and deep learning models to detect unauthorized access or malicious behavior before alerting security professionals.
- Automated threat analysis uses NLP and ML technologies to discover threats through user sentiment in comments, emails, and other in-house communications that could pose a risk.
- AI-driven document classification tools use generative AI to categorize and sort files and documents into safe systems, and the analyze the content and context of each document.
