AI in Cybersecurity: Overview and Use Cases

Businesses and individuals rely on computer systems daily, and the need for robust cybersecurity is at an all-time high. Research by McKinsey shows that organizations spent about $150 billion on cybersecurity in 2021, a 12.4% increase from 2020. Investment in cybersecurity is expected to continue to increase in the coming years.

Cybersecurity is the branch of computer science that works to protect computer systems’ data, networks, hardware, and apps. Threats can be unintentional or malicious, and include phishing attacks, malware, denial of service (DoS) attacks, zero-day exploits, and social engineering.

Artificial intelligence (AI) has helped transform the cybersecurity sector, enabling companies to identify threats earlier and implement preventive measures before vulnerabilities are exploited. Other AI benefits include reducing human error, better resource monitoring, and improved scalability and availability of programs.

While AI has helped improve information security, it still has challenges. Its implementation can be complex, it introduces its own data privacy concerns, and its subject to bias and false positives.

Keep reading to discover how AI is used in cybersecurity, including its applications, benefits, and challenges.

How is AI used in cybersecurity?

Previously, cybersecurity used signature-based detection to identify malicious activities. The system examined network traffic and compared results to specific signatures, then triggered alerts when it found a match. While this system was largely effective, it required the antivirus software to recognize the threat—new viruses with code that wasn’t yet in the database would be overlooked.

As cyber threats evolved, there was a need for more effective intrusion and detection prevention systems to keep user data safe. Machine learning algorithms started gaining popularity in the early 2010s. These AI models could analyze data sets quickly and discover patterns associated with security threats.

Continued investment in cybersecurity and access to big data allowed ML algorithms to grow more effective. More AI-powered tools were introduced to help with threat detection and incident response. These AI systems made it possible to automate numerous processes, helping seal loopholes caused by human error.

Today, technologies like natural language processing (NLP) and deep learning have led to the development of more sophisticated threat prevention tools. Using NLP, computer systems can analyze and process human input and language, making it possible to respond to dynamic cyberthreats.

For example, NLP allows computers to analyze suspicious email content and compare it with known patterns associated with phishing. NLP can also help identify anomalies in user communication patterns, which could indicate compromised accounts or social engineering threats.

Deep learning facilitates the discovery of complex patterns and relationships in data, enabling teams to detect threats at a higher degree of accuracy. ML algorithms are trained on large amounts of data, allowing them to identify abnormal patterns, anomalies, and malicious activities. Companies like IBM and OpenAI have contributed to AI adoption by helping smaller organizations and individuals access machine learning algorithms and large language models (LLMs) like GPT-3 and GPT-4.

While intrusion detection and prevention systems have become more advanced, cyberthreats have also become more sophisticated. Hackers and other individuals harness AI technology to discover vulnerabilities, launch more complex attacks, and increase the scale of their operations. Common threats include:

• Cryptojacking. A victim’s computer systems are used for mining cryptocurrencies—without their knowledge or consent.
• Data breaches. Companies can lose large amounts of data due to exploited vulnerabilities and other threats.
• Denial of service attacks. These cyberattacks prevent normal users from accessing websites, servers, or networks by using bots to direct an overwhelming amount of traffic to these systems.
• Insider threats. Employees can use their access to computer systems to sabotage key processes.
• Malware. Malware includes viruses, spyware, trojans, and worms, which are installed on computer systems.
• Ransomware. This attack involves encrypting a user’s data, making it unusable until the victim pays the hacker.
• Social engineering. This attack involves tricking individuals into divulging sensitive information like login details.
• Unintentional insider threats. An unintentional insider threat (UIT) occurs when someone with access to an organization’s resources accidentally exposes it to threats.
• Zero-day exploits. This cyberattack involves exploiting newly discovered vulnerabilities before they are patched.

Benefits of implementing AI in cybersecurity

AI can analyze large data sets and discover patterns and relationships that humans may miss. This ability makes AI-powered cybersecurity tools capable of dealing with complex and ever-changing threats.

Benefits of implementing artificial intelligence in cybersecurity include:

• Data-driven insights and reporting. AI-powered tools can analyze and process large data sets, providing crucial insight to organizations. With this information, companies can understand their security situation and make informed strategic decisions.
• Enhanced proactive security. Adopting AI in cybersecurity enables organizations to move from a reactive to a proactive security approach. AI-powered tools continuously and autonomously monitor for vulnerabilities, allowing companies to address them before they’re exploited.
• Future-readiness. Cyberthreats are becoming increasingly complex and difficult to identify and prevent. Artificial intelligence helps ensure organizations are ready to deal with these evolving threats.
• Reduction in human error. Artificial intelligence can automate repetitive and routine tasks, minimizing human oversight or errors normally associated with manual processes.
• Resource efficiency. Artificial intelligence helps streamline operations through automation by reducing the reliance on human monitoring and manual interventions. As a result, organizations can cut costs by automating some of their repetitive and time-consuming processes.
• Scalability and adaptability. AI uses algorithms to learn from new data, which allows it to adapt and scale as the company grows—and as cyberthreats evolve. This leads to sustained security efficacy.

Challenges of AI in cybersecurity

Artificial intelligence has improved the cybersecurity sector, making it easier to identify threats and vulnerabilities and prevent them—but it still faces several challenges that affect its efficiency.

We cover some of the downsides of AI in cybersecurity.

• Adversarial attacks. Cybercriminals are also getting smarter by using complex AI-driven techniques to trick or bypass various security measures. For example, hackers can use adversarial machine learning to provide malicious input to trick computer systems into performing specific actions.
• Bias and false positives. AI-powered systems aren’t always accurate. And if they’re not properly trained, they could show bias or produce false positives, causing companies to make ineffective decisions. To counter such problems, there’s a need for diverse and extensive training data.
• Complexities of implementation. Artificial intelligence is still an emerging technology, and not many people have the technical expertise to work with it. Some AI tools also have a steep learning curve, and finding the right talent can be difficult. As a result, businesses might find integrating AI into their operations difficult. Compatibility issues can also arise, making it difficult to add AI to existing systems.
• Data privacy concerns. AI systems require vast amounts of data to discover patterns and make accurate predictions and classifications. As a result, there’s a likelihood of potential data breaches and misuse of personal information. Organizations can also come under scrutiny from regulations like the General Data Protection Regulation (GDPR).
• Overreliance on automation. There’s also the risk of becoming too dependent on artificial intelligence, leading to complacency. When AI tools fail, companies could risk being exploited. Human oversight is required to interpret and act on AI findings correctly.

Use cases

Artificial intelligence has transformed the modern cybersecurity sector, enabling companies to protect computer systems and enhance their security posture. Advanced machine learning algorithms with vast data processing capabilities are helping security teams discover threats and vulnerabilities and deal with challenges.

While AI technology isn’t foolproof, its applications are vital in boosting organizations’ security. We cover practical AI use cases in addressing real-world security challenges.

• Real-time threat detection
• Network and endpoint security enhancement
• Predictive analysis for future threats
• Behavior analysis and user profiling
• Cloud security reinforcement
• Automated incident response
• Phishing detection and prevention
• Deep learning for malware detection
• Natural language processing for open-source threat intelligence

‍

Real-time threat detection

Artificial intelligence enhances real-time threat detection by quickly analyzing large data sets, identifying patterns, and making predictions. AI can analyze how users, systems, and networks operate and detect a deviation from established normal patterns.

AI can also identify complex threats by comparing attack patterns seen in its training data. It can do this in real time, enabling organizations to respond to threats before situations escalate.

Examples of AI-powered cybersecurity tools for real-time threat detection are CrowdStrike Falcon and IBM Security QRadar SOAR.

Network and endpoint security enhancement

AI-powered tools can continuously monitor network traffic patterns and device behavior, allowing the tools to identify malicious activities and trigger necessary actions or alarms. AI can also analyze large volumes of data, enabling it to detect emerging threats and using the information to block new threats that match specific patterns.

Artificial intelligence tools can also help identify zero-day exploits and vulnerabilities—in internal systems and user endpoints—enabling companies to take steps to protect their systems. And in case threats are detected inside the company, AI can help find their sources, allowing organizations to implement network segmentation to reduce the impact of attacks.

Examples of network analysis tools are Malwarebytes for Business and ServicePilot, which use AI technology to help organizations identify and deal with complex threats.

‍Predictive analysis for future threats

Artificial intelligence can analyze current data trends and historical patterns, enabling it to predict future threats and risks. As a result, it can offer predictive insights to organizations, allowing them to make data-driven decisions.

Vectra AI and Darktrace are examples of tools that can help businesses predict and respond to security threats.

Behavior analysis and user profiling

AI cybersecurity tools can analyze user behaviors, interactions, preferences, and actions—and use this information to build a profile about them. As a result, these AI tools can detect when users deviate from their normal routine, which could indicate malicious intent, an unintentional oversight, or compromised accounts.

For example, artificial intelligence can help identify unauthorized login attempts, malicious payment patterns, and fraudulent purchasing behaviors. Tools like LogRhythm and Securonix use machine learning algorithms for behavioral analysis.

Cloud security reinforcement

Apart from the physical environment, AI cybersecurity tools are designed to detect vulnerabilities and anomalies in cloud systems through processes like behavioral analysis, real-time monitoring, log analysis, and predictive analytics. These activities help keep business data secure.

Examples of AI tools for cloud security are IBM Cloud Pak, Microsoft Sentinel, and Amazon GuardDuty.

Automated incident response

AI-driven cybersecurity platforms can automatically detect and respond to threats—without human intervention. These tools can isolate affected systems and initiate predefined security protocols.

Due to the fast AI response, organizations can reduce overall costs. Automation also reduces the burden on cybersecurity personnel, allowing them to focus on other activities.

AI tools like Cynet and Intraspexion can help with automated incident response.

Phishing detection and prevention

Phishing is a social engineering technique that tries to trick individuals into revealing sensitive information like their contact details and passwords. Artificial intelligence can assist in identifying patterns of phishing emails, websites, text messages, and other deceptive tactics.

Symantec Email Security.cloud and GreatHorn are examples of phishing detection and prevention software that businesses can use.

Deep learning for malware detection

Deep learning uses neural networks to identify complex patterns and relationships from data. AI tools powered by deep learning differ from traditional antivirus solutions because they can dig deeper into the malware to check for malicious code, leading to more accurate predictions and classifications.

Traditional antivirus tools rely on predefined signatures to identify malware, which can be ineffective when dealing with rapidly evolving threats. SentinelOne and Cisco’s Talos are examples of platforms that businesses can use for malware detection.

Natural language processing for open-source threat intelligence

Natural language processing (NLP) technology enables computer systems to analyze and process human language. AI-powered cybersecurity tools can use NLP to analyze vast amounts of open data and gather threat intelligence.

Cybersecurity platforms like Kaspersky Threat Intelligence and ZeroFox can provide crucial insights that might be missed by human cybersecurity analysts. With generative AI, cybersecurity tools can process user inputs and produce meaningful outputs, helping stakeholders make informed decisions.

Harness the power of AI in cybersecurity

Artificial intelligence brings numerous benefits to cybersecurity, including the ability to detect complex and dynamic threats faster and respond before the situation worsens.

Powered by machine learning and deep learning algorithms, AI cybersecurity tools make it possible to predict future threats, enabling businesses to make informed strategic decisions. And with NLP technology, AI can analyze speech and text data and provide useful insights for decision-making.

When integrating AI into your workflow, you need people with the right technical skills and knowledge. AI experts can assist in finding the right cybersecurity tools and providing guidance in the AI implementation process.

Upwork can connect you to freelance cybersecurity professionals like internet security specialists, information security analysts, or artificial intelligence engineers to help you with AI adoption.

‍

Upwork does not control, operate, or sponsor the tools or services discussed in this article, which are only provided as potential options. Each reader and company should take the time to adequately analyze and determine the tools or services that would best fit their specific needs and situation.