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Artificial intelligence in cybersecurity

The explosion of artificial intelligence (AI) in cybersecurity is due to its many powerful features, such as continuous learning and adaptation, problem-solving, and massive data-handling capabilities. These advanced features enable AI to process and analyze vast amounts of data at unparalleled speeds, identifying anomalies and potential threats that traditional methods might miss. Powered by artificial intelligence, cybersecurity solutions can provide greater protection than ever before.

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However, the protections against cyber threats made possible by artificial intelligence are simultaneously countered by cybercriminals' adoption of AI. Cybercriminals are harnessing all the power and capabilities of AI to develop more sophisticated attack vectors. This includes automating their malicious activities and increasing the scale of their attacks, as well as creating dynamic attack models that are more effective and harder to detect.

Despite being co-opted by threat actors, artificial intelligence remains a critical component of sophisticated cybersecurity. Artificial intelligence allows security teams to stay ahead of malicious activity from cybercriminals and insider threats.

This article will review the tremendous advantages of artificial intelligence in cybersecurity. Learn how AI can be used to help detect cybersecurity threats, identify the presence of bots, predict data breaches, and support secure remote workforces. It will also review the disadvantages of artificial intelligence in cybersecurity, including how cybercriminals are using it.

Advantages of artificial intelligence in cybersecurity

Improve scalability

Leveraging artificial intelligence in cybersecurity, systems can more quickly and easily scale to meet the demands to process and analyze growing datasets generated from disparate sources. This includes Internet of Things (IoT) sensor data, network traffic logs, system logs, threat intelligence feeds, and user behavior data.

With the ability to scale quickly, AI-powered cybersecurity solutions have more opportunities to detect hidden vulnerabilities and threats.

In addition, the integration of AI into cybersecurity solutions allows organizations to more efficiently manage growing volumes of data from other security systems and data sources. AI-powered cybersecurity systems can also continuously adapt and learn from new data. This ability to dynamically adapt improves their threat detection capabilities over time.

Increase threat visibility

Artificial intelligence in cybersecurity increases the reach and accuracy of traditional security solutions. The vast volumes of information that AI-powered solutions can process enable them to detect patterns indicating unusual activity that people and other systems could not find.

By analyzing data in real-time from multiple sources, AI can identify subtle anomalies that may indicate a threat, allowing for quicker intervention. Additionally, machine learning algorithms can predict potential security breaches by recognizing trends and behaviors that precede an attack, thus giving organizations a proactive edge.

The ability of AI-powered cybersecurity solutions to continuously learn and adapt ensures that security measures evolve as new and evolving threats are detected. This enhanced visibility can not only help reduce the number of false positives but also increase the efficacy of threat detection and response systems.

Optimize risk management

Risk management efforts can be enhanced and optimized with artificial intelligence in cybersecurity. The detection and processing capabilities of AI make it possible to sift through previously opaque data sets to find hidden risks and gaps in security. By identifying more vulnerabilities, AI-powered cybersecurity solutions enable proactive risk management, stopping or mitigating risk before an incident can happen.

AI-powered cybersecurity solutions have the unique ability to learn and adapt continuously. This allows them to predict future threats based on historical data and emerging patterns, allowing for more dynamic and forward-thinking risk mitigation strategies. This proactive approach ensures that potential vulnerabilities are addressed before cybercriminals can exploit them.

Risk management is further optimized by the ability of AI-powered cybersecurity tools to automate the prioritization of threats. This reduces the burden on security analysts and helps ensure that the most critical issues are addressed promptly.

Reduce bandwidth drain on security teams

Many tedious security tasks and resource-intensive functions (e.g., log analysis, patch management, and vulnerability assessments) can be handled with artificial intelligence in cybersecurity. This allows scarce and valuable security teams to focus their time and energy on other areas that are better served by the nuanced capabilities of people. It also can prevent human errors that can be costly and increase risk.

Speed threat detection and response

With artificial intelligence in cybersecurity, systems can identify and respond to anomalies, behavioral patterns, and other indicators of compromise in devices, endpoints, networks, and other systems in real time. The broad range and scale of threats that can be detected in real-time make it possible to stop previously elusive zero-day attacks.

As soon as a threat indicator is detected, AI-powered cybersecurity solutions can automate incident response procedures, such as blocking malicious traffic, isolating infected systems, and redirecting traffic from sensitive systems. This rapid response capability not only helps mitigate potential damage but also decreases downtime and recovery costs.

Streamline compliance efforts

The automation provided by artificial intelligence in cybersecurity helps organizations streamline compliance efforts with support for data protection and data privacy requirements set forth in regulations such as General Data Protection Regulation (GDPR) and Health Insurance Portability and Accountability Act (HIPAA). These solutions can also automate monitoring and reporting.

Artificial intelligence capabilities used for cybersecurity

Artificial intelligence in cybersecurity enables a number of approaches and tactics for detecting cyber threats, including the following.

Generative AI and large language models (LLMs)

Generative AI systems are powered by large language models, which are deep learning algorithms that use natural language processing (NLP) and are trained on volumes of internet data. When used with artificial intelligence in cybersecurity, generative AI can provide a contextual understanding of attacks that enables defenses to be optimized and proactive.

These models can analyze patterns and generate responses that adapt to the changing tactics of cybercriminals. By constantly learning from vast data inputs, generative AI systems can identify new threats and even predict potential future attack vectors, allowing organizations to strengthen their defenses preemptively.

Self-learning AI

Self-learning AI is optimal artificial intelligence in cybersecurity. Unlike traditional algorithms, which require predefined parameters and extensive labeled datasets, AI-powered cybersecurity solutions can train themselves using unlabeled data, continuously refining their defense mechanisms. This allows these solutions to learn to fill in blanks when limited training data is available, as is the case with nascent and zero-day attacks, insider threats, and generative AI attacks.

Supervised machine learning with known attack data

Vast amounts of data related to known attacks can be used to train supervised machine-learning models. Using information about how attacks were perpetrated and attacker behavior patterns, supervised machine learning models are optimized to predict and proactively stop future attacks.

Extended detection and response (XDR) systems are among those that use this type of artificial intelligence in cybersecurity. These systems can leverage historical data to identify patterns that signify potential threats, enhancing their ability to detect and mitigate risks in real time. By continuously learning from new data, AI-powered XDR systems remain updated and more effective against evolving threats, offering a dynamic layer of protection.

This predictive capability enabled with supervised machine learning models is crucial, as it allows organizations not only to react but to better anticipate and neutralize threats before they cause significant harm. Furthermore, supervised machine learning models can automate routine cyber defense tasks, freeing up valuable resources and allowing cybersecurity teams to focus on more complex issues that require human intervention.

How is AI used in cybersecurity?

AI has become an important component in cybersecurity solutions. The following are several of the many use cases for AI in cybersecurity.

AI-assisted cyber threat intelligence

Threat hunting and threat intelligence efforts that support cybersecurity initiatives are materially enhanced with artificial intelligence, such as using deep neural networks to uncover cyber threats such as phishing messages, ransomware, and malware.

AI can be used to facilitate the collection, processing, and enrichment of threat-related data from internal systems and third-party sources (e.g., threat intelligence feeds). It can also be used to identify emerging threats and create threat profiles by correlating and contextualizing threat data.

Threat-hunting efforts also benefit from artificial intelligence. With AI-powered advanced analytics and automation, security analysts can more efficiently search for unknown and hidden threats and vulnerabilities. These tools can also help prioritize threats to optimize responses.

Anomaly detection

The identification of unusual patterns in anything from logs and network traffic to user behavior and API calls is an ideal use case for artificial intelligence. AI-powered cybersecurity tools excel at real-time monitoring and alerting when anomalous activity is detected.

Not only is artificial intelligence in cybersecurity able to spot patterns in massive volumes of data, but they are also able to categorize and prioritize them. By automatically assigning priority levels to security events, AI-powered solutions prevent alert fatigue, which results in the oversight of potential or in-process incidents due to security analysts being overwhelmed with alerts.

Cybersecurity training and development with AI

Artificial intelligence is increasingly being used to support scenario-driven cybersecurity training based on real-world threats. Using generative AI, cybersecurity training can be created that presents realistic, scenario-based simulations. These AI-generated scenarios adapt in real-time, reflecting the evolving nature of cyber threats, thus providing a practical, immersive, and highly engaging security training experience.

This AI-supported cybersecurity training can be used to teach security teams to respond to dynamic cyber threats. Security teams have opportunities to engage with various attack vectors and defense strategies, increasing their understanding of how they work, building deep technical expertise, and enhancing their ability to think critically and react swiftly under pressure.

This type of training can also be tailored for end users. Security training programs can be created that simulate real-world scenarios and can also be used to develop tests to assess end users’ ability to detect threats, such as phishing emails.

Identifying bots with artificial intelligence

Artificial intelligence in cybersecurity systems used for bot detection is tasked with distinguishing human-generated activity from automated activity executed by bots. AI-powered tactics used to make this distinction include:

Bot pattern identification
CAPTCHA (Completely Automated Public Turing test to tell Computers and Humans Apart) challenges
Internet protocol (IP) analysis
Traffic analysis
User behavior analysis

Potential benefits of using artificial intelligence in cybersecurity solutions aimed at detecting bots include:

Ability to detect highly sophisticated and zero-day threats
Automated, real-time incident response
Dynamic adaptability
Holistic, real-time analysis
Increase in power and efficacy with continuous learning and evolution
Proactive detection
Reduction in false positives
Scalability

Methods of using artificial intelligence in cybersecurity solutions that detect bots include:

Automated threat monitoring and incident response
Cyber threat prediction
Data-driven decision-making for risk management and advanced cybersecurity policies

Predicting data breaches with artificial intelligence

Leveraging artificial intelligence in cybersecurity solutions aimed at predicting and preventing data breaches can result in faster, broader threat detection. AI solutions have significantly reduced data breach risks with proactive threat prediction, identification, and response, minimizing the success and impact of data breach attacks.

Using AI-driven predictive analytics, data breach attack vectors can be proactively identified by:

Analyzing vast amounts of data in real-time, including network traffic, user behavior data, and log data from different systems
Learning and evolving based on new threat profiles and behavior
Monitoring network activity continuously to detect threats
Using advanced machine learning algorithms allows AI systems to learn from patterns, anomalies, and suspicious activity

Artificial intelligence for secure remote work

The two biggest roles of artificial intelligence in functions related to remote work are cybersecurity and compliance monitoring.

The AI-powered cybersecurity solutions deployed for enterprise environments are extended and applied to remote users with adaptations that take into account differences, such as extensive mobile device use and the need for secure connections from remote locations.

The robust monitoring and detection capabilities of AI solutions are very effective in enabling compliance with regulatory and internal requirements.

Security log analysis

When analyzing security log data, artificial intelligence in cybersecurity uses machine learning algorithms to process vast amounts of raw information and distill it into insights. AI-driven security log analysis detects suspicious patterns and anomalies that are part of known threat signatures. With this use of artificial intelligence in cybersecurity, user behavior data can be ingested from multiple applications and systems to identify potential insider threats.

An AI-powered cybersecurity solution can continuously learn from the log data it analyzes, improving its ability to recognize and predict new types of attacks. Additionally, these tools can correlate information from different logs and sources, providing a comprehensive view of an organization's security posture. This holistic approach helps ensure that even the most subtle indicators of a threat are not buried in massive volumes of data in log files.

Threat detection and prevention

Artificial intelligence in cybersecurity is widely used for threat detection and prevention (e.g., malware and phishing). Because of its capacity to analyze data and identify patterns, AI-powered tools can proactively identify threats and trigger automated alerts to neutralize them.

These powerful solutions typically get better with time, evolving and adapting to recognize signs of sophisticated attacks (e.g., spear phishing) and newly emerging attacks. Threat detection tools that leverage AI can also more efficiently distinguish between benign and malicious activities by cross-referencing vast datasets and identifying subtle anomalies that may indicate a potential threat, as well as filtering out false positives.

Disadvantages of artificial intelligence in cybersecurity

The efficacy of artificial intelligence in cybersecurity solutions is undeniable. However, there are several notable disadvantages to be aware of in order to mitigate the related risks. These include:

An AI skills gap can make it challenging to find and recruit people to run the systems.
An inability to explain how results were generated restricts transparency.
Bias and discrimination due to data inputs can negatively influence decision-making.
Generative AI tools can lead to unintentional intellectual property loss or data leakage, resulting in data security and privacy risks.
LLM’s prompt-based models are susceptible to injection attacks.

Artificial intelligence in cybersecurity takes defense to a new level

The sheer power of artificial intelligence in cybersecurity solutions, coupled with its ability to learn and evolve, allows organizations to take a proactive approach to defenses. From fending off advanced persistent threats (APTs) and zero-day attacks to stopping phishing and malware attacks from hitting emails, AI-driven cybersecurity solutions often succeed in thwarting breaches. Despite its challenges, AI belongs in every organization’s cybersecurity solution portfolio.

Artificial intelligence in cybersecurity FAQ

What are examples of the types of cybersecurity solutions that use artificial intelligence?

AI-powered cybersecurity solutions fall into several broad categories, each focusing on a different aspect of security. The following examples illustrate how AI is being used for cybersecurity.

Data Loss Prevention (DLP)

AI enhances DLP solutions by analyzing large volumes of data, identifying sensitive information that might be exposed or shared inappropriately, and detecting unauthorized access or suspicious activities related to data leakage. These solutions are widely used to protect confidential data and intellectual property, as well as prevent accidental or malicious data breaches.

Malware detection and prevention

AI-powered solutions in this category analyze files, applications, and data flows to identify potential malware based on behavior rather than traditional signatures. They can also predict and block new types of malware, such as ransomware and fileless attacks, before execution. Malware detection and prevention tools that use AI include antivirus software, anti-malware, and next-generation firewalls.

Network traffic analysis (NTA)

NTA tools use AI to monitor and analyze network traffic and detect suspicious or anomalous behavior that could indicate a cyber threat. Some types of NTA tools powered by AI include network behavior anomaly detection (NBAD) systems, firewalls, and network detection and response (NDR).

Threat detection and response

These solutions identify, analyze, and respond to potential cyber threats in real time, including zero-day exploits or advanced persistent threats (APTs). Among the many tools in this category are security information and event management (SIEM), intrusion detection and prevention systems (IDPS), and endpoint detection and response (EDR).

User and entity behavior analytics (UEBA)

AI models in UEBA solutions learn the normal behavior of users and entities (e.g., devices and applications) to detect deviations that may suggest insider threats, compromised accounts, or other malicious activities. These tools enable insider threat detection, anomalous behavior detection, and identity and access management systems.

What is responsible AI in cybersecurity?

Responsible AI means that AI systems, including AI-powered cybersecurity solutions, are developed, used, and deployed in a way that is ethical, legal, and safe. The objectives of responsible AI are to ensure that AI is used in beneficial ways and to do as much as possible to avoid inadvertent harm and unintended consequences. The core principles of responsible AI that are meant to provide guardrails around its use include the following.

Accountability

Developers and organizations using AI for cybersecurity should be accountable for the outcomes and impacts of their AI systems. Governance structures should be in place to ensure ethical oversight, including mechanisms to address issues that arise from AI use. Human involvement is essential, particularly in high-stakes decision-making processes.

Fairness and non-discrimination

AI systems should be designed and used in ways that promote fairness and prevent discrimination. Homogeneous training data can create biases in algorithms that can lead to unfair outcomes that disproportionately affect certain groups. Developers should ensure that AI models are trained on diverse datasets and regularly audited to reduce bias.

Privacy and security

AI systems must respect individuals’ privacy and data security. This includes adhering to data protection regulations, implementing strong cybersecurity measures, and ensuring that AI systems handle sensitive information responsibly. In addition, AI-powered cybersecurity solutions must adhere to data security and privacy rules set forth by regulations, industry standards, and internal policies.

Reliability and safety

AI-powered cybersecurity solutions should be designed to operate safely, reliably, and as intended. Rigorous testing, validation, and regular monitoring should be part of the development cycle to ensure systems perform well under different conditions, can respond correctly to unanticipated conditions, do not pose risks, and resist harmful manipulation.

Transparency and Explainability

When AI is built into cybersecurity systems, there should be transparency about how decisions are made. This includes making AI models explainable to users and stakeholders and ensuring that those impacted by AI-driven decisions understand the rationale behind them. In the event of performance issues, fairness issues, exclusionary practices, or unintended outcomes, developers need to be able to identify the root cause.

What is the role of AI in cybercrime?

Artificial intelligence plays a dual role in the realm of cybercrime. It enables cybercrime and plays a critical role in preventing and mitigating it. Both cybercriminals and cybersecurity solution providers leverage their capacity for automation, data analysis, and pattern recognition to achieve their objectives.

AI for enabling cybercrime

Cybercriminals are increasingly using AI to enhance the sophistication and scale of their attacks. With AI, attackers can develop and automate sophisticated attacks.

For example, AI-powered phishing attacks can craft highly personalized emails that trick users into revealing sensitive information. These attacks use AI to scan social media profiles and public data to create convincing bait, significantly increasing the likelihood of success.

AI is also used to create dynamic malware capable of evading detection. Machine learning (ML) models can help malware adapt to traditional cybersecurity defenses, such as firewalls and antivirus software.

AI-powered malware can analyze a target environment, modify its behavior to avoid detection and identify and exploit vulnerabilities in real time. This adaptability makes AI-powered attacks more difficult to identify and mitigate.

Another common use of AI in cybercrime is the exploitation of automated tools to conduct distributed denial of service (DDoS) attacks. AI algorithms can create, grow, and help manage botnets more efficiently, directing vast numbers of compromised devices to flood and overwhelm targets faster and with greater precision.

AI also helps cybercriminals crack passwords more effectively using brute-force attacks. Using AI, these attacks can predict common password patterns or generate potential credentials.

AI for fighting cybercrime

While criminals use AI, it also powers highly effective cybersecurity tools. AI-powered cybersecurity solutions can detect unusual behavior or patterns in network traffic that might indicate a cyber attack. Machine learning algorithms can analyze vast amounts of data to identify threats in real time, offering cybersecurity teams early warning and automatic threat mitigation capabilities. Additionally, AI-powered threat intelligence systems can quickly learn from previous attacks, evolving defenses to counter new types of threats.

AI-driven automated security tools can also be used for threat hunting, scanning systems for vulnerabilities, and predicting potential attack vectors based on historical data. This proactive approach helps organizations stay one step ahead of cybercriminals.

Will AI replace cybersecurity?

No, we believe that artificial intelligence will not replace cybersecurity. We believe it will complement and enhance cybersecurity rather than replace cybersecurity professionals.

While AI brings incredibly powerful capabilities to the field, human expertise remains crucial for understanding and managing complex security challenges. Cybersecurity requires not just technical defenses but also strategic decision-making, ethical considerations, and legal compliance, which require human judgment. AI may automate many aspects of cybersecurity, but humans are still needed to interpret complex threats, make decisions in ambiguous situations, and oversee the ethical implications of using AI in security.

AI excels in automating routine tasks, such as detecting potential threats, analyzing large amounts of data, and responding to known vulnerabilities. It can quickly identify patterns and anomalies in network traffic, which helps flag suspicious activities that might indicate a cyber attack. AI can operate faster than human teams, reducing the response time to security incidents and minimizing potential damage. Machine learning models can also learn from past attacks, improving threat detection over time.

However, AI has its limitations and cannot fully replace human involvement. It is not able to understand the full context of sophisticated or novel cyber threats, especially as cybercriminals continuously evolve their tactics. AI systems are also vulnerable to manipulation, such as adversarial attacks, where attackers input deceptive data to trick the AI into making incorrect decisions. Additionally, AI relies heavily on the quality of the data it is trained on, with poor or biased data resulting in false positives or missed threats that can result in security breaches.

Date: March 4, 2025Reading time: 15 minutes

AI & Machine LearningSecurity

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