AI in Cybersecurity: Exploring the Top 6 Use Cases

As technology progresses, so do the threats posed by hackers and cyber attackers. According to the FBI Internet Crime Report, 847,376 complaints of internet-related crimes and financial losses exceeding 6.9 billion dollars, a substantial increase from the previous year.

Cyber crimes such as phishing, scams, and data theft are increasing, putting organizations at risk. To combat these threats, organizations are turning to qualified cybersecurity teams equipped with cutting-edge technologies, including artificial intelligence (AI) that swiftly detect and counter malicious activities, fortifying networks against threats.

The recognition of AI's potential has led 76% of enterprises to prioritize AI and machine learning in their IT budgets, driven by the immense volume of data that necessitates analysis to identify and combat cyber threats effectively. With connected devices projected to generate a staggering 79 zettabytes of data by 2025, manual analysis by humans becomes impractical, making AI an indispensable tool in the fight against cybercrime.

In this article, we explore the critical role of artificial intelligence in cybersecurity, its advantages, challenges and real-time use cases from the biggest worldwide companies like AWS or PayPal.

Importance of AI in Cybersecurity

According to Verified Market Research, the market size for AI in cybersecurity was $17 billion in 2022 and is projected to reach an impressive $102 billion by 2032. These figures are hardly surprising, considering that hackers also leverage new technologies in their malicious pursuits.

The escalating frequency of cyber-attacks has sparked international attention toward the potential use of AI in cybersecurity. A survey by The Economist Intelligence Unit revealed that 48.9% of global executives and security experts consider AI and machine learning as potent tools to combat modern cyber threats. Furthermore, Pillsbury's report highlighted that 44% of global organizations are already leveraging AI to detect security intrusions.

AI in cybersecurity establishes secure applications by default, eliminating vulnerabilities for users. By eradicating negative defaults, AI guarantees precision in detecting issues, expediting investigations, and automating response mechanisms. AI-driven solutions, such as user verification through behavioral biometrics, foster secure app development and promote a safe data ecosystem, contributing to a robust infrastructure.

AI can identify potentially malicious activities and threat actors, allowing organizations to predict and prevent cyber attacks before they occur. With AI-enabled automated monitoring, systems can remain safeguarded around the clock, empowering organizations to take proactive measures to protect their digital assets before any harm is done.

Is AI important for cyber security?

The escalating complexity of cyber threats, includingsocial engineering and ransomware, poses challenges for conventional defenses in effectively detecting and preventing such attacks. As organizations confront an overwhelming volume of data requiring analysis for potential risks, strengthening cybersecurity becomes crucial. Adopting innovative solutions is imperative to effectively combat these threats.

Cost Reduction
AI-driven automation leads to cost reductions in various areas of cybersecurity operations. By automating routine tasks such as log analysis, vulnerability assessments, and patch management, AI minimizes the need for manual intervention, saving valuable time and human resources.

AI's ability to improve threat detection accuracy also contributes to cost reduction. Traditional security approaches may generate false positives or miss certain threats, leading to wasted time and resources investigating non-existent issues or overlooking actual security incidents.

Improved Scalability
Traditional cybersecurity approaches often struggle to handle the vast volumes of data generated in complex and interconnected environments. AI excels in scalability, processing and analyzing massive amounts of data from various sources simultaneously.

AI algorithms can effectively analyze network traffic logs, system logs, user behaviors, and threat intelligence feeds. This scalability allows AI to detect subtle indicators of cyber threats that may escape human analysts, ensuring a proactive defense posture.

As reported by Forbes, 2022 was marked by a turbulent and eventful year in cyber security worldwide. Global cyberattacks surged by a staggering 125% through 2021 and continued their relentless growth, posing threats to businesses and individuals throughout 2022. The consequences were dire, with businesses incurring an average cost of $4.35 million to address and mitigate data breaches effectively.

According to Threat Landscape (ETL) report in October 2022, the primary threats and major trends shaping the landscape are:

• The most dangerous threats include ransomware, malware, social engineering, denial of service, or distributed denial of service attacks (DoS, DDoS), particularly affecting the supply chain, and misinformation and disinformation campaigns over the Internet.
• The most targeted sectors encompass governments (24%), public services (12.5%), private services (12%), IT providers (13%), banking/insurance (9%), and health (7%).
• The threat of cybercrime has pushed malicious actors to exploit new tactics and technologies, lowering the entry barriers for cyber attacks. Cybercriminals now offer subscription services and starter kits, escalating the challenge of cybersecurity. The utilization of large language models like ChatGPT for writing malicious code further underscores the potential risks in the digital landscape.

Is AI going to replace cyber security?

In the pre-AI era, cybersecurity heavily relied on signature-based detection systems as its primary defense against threats. These systems compared incoming network traffic to a predefined database of known threat signatures. When a match was found, the system would raise an alert and take measures to block or contain the identified threat.

To compound the challenges, manual analysis was central to traditional cybersecurity operations. Security analysts meticulously investigate security alerts and log data, searching for patterns or indicators of potential security breaches. This painstaking process was highly time-consuming and heavily reliant on the expertise of individual security analysts to identify threats accurately.

Rule-based systems, another component of the traditional approach, operate by establishing rigid rules and policies that define acceptable network behavior. Any deviation from these rules would trigger an alert. While these systems had some effectiveness in specific scenarios, they were often inflexible and struggled to adapt to the new cyber threats.

A difference in AI from traditional cybersecurity approaches lies in its capacity to continuously learn and adapt. AI systems remain agile, continually learning from new data to enhance their detection and response capabilities.

Use Сases of Artificial Intelligence in Cyber Security
According to Forbes, 76% of enterprises have already prioritized AI and machine learning in their IT budgets, indicating the widespread recognition of AI's value. Here are some key advantages of integrating AI into cybersecurity:

Use Case 1: Threat Detection and Prevention

One area where AI excels is threat detection. It can analyze large amounts of data from different sources and identify unusual patterns in users' behavior, which could indicate a cyber attack. For example, if an employee unknowingly clicks on a phishing email, AI can quickly notice the change in their behavior and alert us to a potential security breach.

When a potential threat is detected, AI-powered systems trigger real-time alerts and notifications to security teams, enabling prompt and effective responses. By automating incident response actions, such as isolating affected systems or blocking malicious activities, AI minimizes opportunities for attackers and limits the potential impact of a security breach.

Malware and Phishing Detection

AI-based cybersecurity systems demonstrate showcasing enhanced efficacy. Deep Instinct's Chuck Everette reveals that AI systems boast security rates of 80% to 92%, surpassing the 30% to 60% achieved by legacy signature-based malware detection systems.

AI analyzes email content and context to differentiate between spam, phishing attempts, and legitimate messages. Machine learning algorithms enable AI to evolve and adapt to new threats, recognizing signs of sophisticated attacks like spear phishing. Intercepting suspicious activities before they harm corporate networks is paramount.

AI systems excel at detecting phishing traps, thus thwarting potential threats. Researchers from the University of North Dakota proposed a machine learning-based phishing detection technique, achieving an impressive 94% accuracy in classifying emails as legitimate or phishing.

Security Log Analysis

AI transforms security log analysis by harnessing machine learning algorithms to analyze copious amounts of real-time log data. By detecting patterns and anomalies, even without known threat signatures, AI empowers organizations to identify and respond to potential security breaches swiftly. Moreover, AI excels at detecting potential insider threats through a comprehensive analysis of user behavior across multiple systems and applications.

Endpoint Security

As remote work becomes more prevalent, securing endpoints becomes paramount in maintaining robust cybersecurity. Traditional antivirus solutions and VPNs rely on signature-based detection, which may lag behind emerging threats, leaving endpoints vulnerable.

AI-driven endpoint protection takes a dynamic approach, establishing baselines of normal endpoint behavior and detecting deviations in real time. By continuously learning from network behavior, AI can identify potential threats, including zero-day attacks, without needing signature updates.

With AI, organizations can enhance password protection and user account security through advanced authentication methods. AI-driven solutions like CAPTCHA, facial recognition, and fingerprint scanners automatically detect genuine login attempts.

Encryption

But what about encryption, which keeps our data safe? Can AI break it? Well, that's a complex question. Encrypted data is like a puzzle, and AI needs to figure out which pieces are good or bad. Breaking encryption is tough because it relies on complex math that even AI struggles with.

The good news is that encryption algorithms, like AES and SHA, are designed to be really tough to crack. They use tricks that make it hard for AI or any attacker to predict how they work. So, even though AI can do amazing things, breaking strong encryption is still a big challenge.

Thread detection in Honeywell

With the help of AI, Honeywell's platform can swiftly analyze vast amounts of data from industrial control systems, identifying any unusual patterns or behaviors that might indicate a cyber threat. This enables the system to proactively detect and block malicious traffic attempting to breach the control systems.

Moreover, the AI-driven platform is designed to continuously learn from past incidents and adapt to new emerging threats. It can recognize patterns associated with unauthorized access attempts and promptly mitigate them before any significant damage occurs.

Use Case 2: User Behavior Analytics

AI models utilize deep and machine learning techniques to analyze network behavior and detect deviations from the norm continuously. Over time, these models self-correct and adapt, improving their accuracy in identifying anomalies and potential threats. The self-correcting nature of AI models empowers organizations with robust and reliable cybersecurity defense mechanisms capable of quickly responding to emerging cyber threats.

AI-driven behavioral analytics enhances threat-hunting processes by creating deployed application profiles and analyzing vast user and device data. This proactive approach enables organizations to identify evolving threats and vulnerabilities effectively.

User Behavior Analytics in Amazon

Through their Amazon Web Services (AWS) platform, Amazon offers various AI-powered security services that have revolutionized how businesses approach threat detection and prevention.

• One such service is AWS GuardDuty, a managed threat detection system that analyzes various data sources, including AWS CloudTrail logs, VPC Flow Logs, and DNS logs, to detect any abnormal behavior that may indicate a security breach. This includes identifying unusual spikes in API calls, atypical network traffic patterns, and unauthorized access attempts to sensitive data.
• Another valuable AI-powered service from AWS is AWS Inspector. This continuous monitoring solution assists in identifying security vulnerabilities within an organization's AWS infrastructure.
• Furthermore, AWS Macie, another innovative offering, is a fully managed data security service that utilizes machine learning to discover, classify, and protect sensitive data within an AWS environment. Macie performs in-depth data analysis, enabling it to identify critical information like personally identifiable information (PII), financial data, and intellectual property (IP).

Use Case 3: Advanced Threat Response and Mitigation

AI's automation capabilities extend beyond detection, facilitating automatic responses to various cyber threats from different vectors. Organizations can leverage AI-enhanced cybersecurity solutions to rebalance the workload on security teams and optimize incident response times. By mining vast amounts of security data and correlating information, AI autonomously generates informed cyber threat responses aligned with technical logs, network traffic patterns, and global threat intelligence.

Advanced Threat Response and Mitigation in Wells Fargo
At the core of Wells Fargo's cybersecurity strategy lies an AI-powered threat detection and response platform. This platform utilizes advanced machine learning algorithms to analyze vast amounts of data, including network traffic, email communications, and files. By processing this data in real-time, the AI system can identify patterns and anomalies that may indicate malicious activities.

Once a potential threat is detected, Wells Fargo's AI system can automatically trigger proactive response measures. For instance, it can promptly block malicious traffic or isolate infected files, preventing the threat from spreading further across the organization's network.

Use Case 4: Vulnerability Assessment and Management

As cybercriminals continuously deploy sophisticated methods, organizations struggle to manage the influx of new vulnerabilities. AI-driven solutions, such as User and Entity Behavior Analytics (UEBA), analyze device, server, and user activities to detect anomalies and zero-day attacks. By preemptively protecting against undisclosed vulnerabilities, AI enables real-time defense against high-risk threats.

Vulnerability Assessment and Management in Splunk

The Splunk Enterprise Security platform leverages machine learning algorithms to analyze vast amounts of data from various sources, including network logs, system events, and user activity. This AI-driven approach allows the platform to detect patterns and anomalies that could signify potential vulnerabilities or malicious activities in real-time.

One of the key advantages of Splunk's AI-driven Vulnerability Assessment and Management lies in its ability to prioritize threats intelligently. By analyzing data with AI algorithms, the platform can accurately assess the severity and impact of each vulnerability, enabling security teams to focus their efforts on addressing the most critical risks promptly.

Use Case 5: Security Operations and Automation

An AI-powered automated threat detection solution processes billions of network requests, endpoint activities, user behaviours, and data points daily. This real-time analysis enables instant action within minutes, a feat that could otherwise take hours or days using manual methods. According to IBM, AI can slash the time taken to detect and respond to cyber threats by a staggering 14 weeks.

AI can autonomously scan systems and networks for weaknesses, streamlining the identification of potential entry points for attackers. By recommending and prioritizing necessary security updates, AI reduces manual effort and minimizes vulnerability exposure. For example, IBM’s managed security services team used these AI capabilities to automate 70% of alert closures and speed up their threat management timeline by more than 50% within the first year of use.

AI automates security operations, streamlining threat identification and elimination processes. Its intervention reduces response time, minimizing the risk of human errors in handling critical tasks. This automation frees cybersecurity professionals to focus on strategic decision-making and enhance defense capabilities.

Security Operations and Automation in Plaid

Plaid utilizes advanced machine learning algorithms to analyze many data points, including the customer's name, address, and Social Security number, among other factors. The AI system can perform accurate and seamless bank account identification and verification in seconds, reducing the potential for errors or fraudulent activities.

Now it can streamline the onboarding process for financial institutions and their customers. Plaid's AI-driven platform eliminates the need for manual intervention and extensive paperwork, thereby accelerating customer onboarding while enhancing overall data security..

Use Case 6: Threat Intelligence and Predictive Analytics

AI systems contribute significantly to breach risk prediction by providing accurate and detailed IT asset inventories. These inventories encompass all devices, users, and applications with varying access levels to critical systems. By combining asset inventory data with threat exposure assessments, AI can predict the areas most susceptible to cyber breaches.

With AI's ability to collect and process diverse data sources, security teams gain a holistic view of the organization's security posture. This enhanced situational awareness enables proactive threat hunting, accurate risk assessments, and timely incident response, strengthening the organization's overall cybersecurity strategy.

Threat Intelligence and Predictive Analytics in PayPal

One of the key applications of AI in PayPal's cybersecurity strategy is transaction analysis. With the sheer volume of daily transactions occurring on the platform, manual scrutiny for signs of fraud would be a monumental task. Here, AI's rapid processing capacities shine as it efficiently examines each transaction for potential red flags.

Furthermore, PayPal extends its vigilance to identifying and blocking malicious websites. As cyber criminals continuously devise new tactics to deceive users through phishing and scam websites, system diligently scans websites to identify malicious content or signs of potential cyber threats.

When not to use AI in Cyber Security?

While artificial intelligence (AI) has become a powerful tool in cybersecurity, there are situations where it may not be the best choice. Here are some scenarios when it might be wise to avoid using AI in cybersecurity:

• If you have a small or outdated dataset, AI may not perform effectively. In such cases, traditional rule-based systems or expert analysis might be more appropriate.
• If your organization lacks the necessary skills or resources, AI adoption can be challenging and error-prone.
• If your company relies heavily on legacy infrastructure, transitioning to AI-based cybersecurity solutions can be challenging and costly.
• If your organization lacks the necessary hardware or cloud resources, AI deployment may be impractical.

Challenges of Implementing AI in Cybersecurity

While AI offers immense potential in bolstering cybersecurity, it has its challenges. Understanding and mitigating these obstacles is crucial for harnessing AI's power effectively. Let's explore some key challenges:

1. Bias in AI security systems

Just like humans, AI systems can be influenced by bias in the data they are trained on. If the training data is biased, the AI system may produce discriminatory outcomes, impacting cybersecurity decision-making. Leading AI platforms invest in ongoing and thoughtful ML training to minimize bias in their systems and ensure fairer results.

2. Misinterpretation

Even advanced AI systems are susceptible to what is known as "AI hallucinations." These systems may misinterpret information and make decisions based on incomplete or false data from their training. As a consequence, incorrect threat assessments could occur, potentially leaving threats undetected or increasing the number of false positives, leading to legitimate operations being blocked or authorized users being denied access.

3. Overreliance

Overreliance on AI may create opportunities for AI-driven errors to accumulate and impact cybersecurity systems, rendering the organization vulnerable to novel cyber-attacks exploiting AI-managed defenses. Human oversight remains indispensable for ensuring optimal outcomes.

4. Cybersecurity skills gap

The shortage of skilled cybersecurity professionals and IT specialists capable of effectively deploying and managing AI systems poses a challenge. Poor implementation, misconfigurations, and inadequate protection against cyber threats may result from insufficient expertise.

5. Privacy and legal сomplications

AI applications in cybersecurity may involve the processing and analysis of vast amounts of personally identifiable data, raising critical privacy concerns. Before deploying AI systems, a legal examination is essential to ensure compliance with privacy regulations. In some regions, AI solutions, such as ChatGPT, may face restrictions, which can complicate deploying other AI-based cybersecurity solutions in those locations.

6. Data unavailability and manipulation

AI systems rely on historical data to recognize patterns and make informed decisions. However, this dependence on data makes them vulnerable to manipulation by malicious actors. Hackers may gain access to the training data and introduce biases, compromising the efficiency and accuracy of AI models.

The Future of Cybersecurity and AI

The software landscape presents an alarming rise in new vulnerabilities, reaching over 22,000 in 2022, the highest reported figure in over a decade. The challenge of staying ahead of ever-mutating threats overwhelms cybersecurity professionals. However, the advent of machine learning-based cybersecurity systems offers a ray of hope.

Tech giants like Google, IBM, and Microsoft are at the forefront, spearheading advanced AI systems for threat identification and mitigation. Google's Project Zero is committed to investing $10 billion over five years to enhance cybersecurity. Their team relentlessly hunts for and fixes web vulnerabilities to safeguard the internet. Additionally, Google Play Protect scans over 100 billion apps for malware and cyber threats.

Microsoft's Cyber Signals program employs AI to analyze a staggering 24 trillion security signals, monitoring 40 nation-state groups and 140 hacker groups. This vigilance allows it to detect malicious activity and software-related weaknesses, thwarting over 35.7 billion phishing attacks and 25.6 billion identity theft attempts on enterprise accounts.

Final Thoughts

The future of consumer cybersecurity hinges on AI, especially when addressing the vast scale and potential threats posed by social engineering and IoT malware. With AI at its core, the cybersecurity landscape is poised to witness unprecedented levels of security, faster response times, and an adaptive defense system.

The power of AI lies in its constant learning capabilities, which outpace manual detection methods employed by human experts. As AI models continuously adapt to new threats, their efficiency in thwarting cyber attacks becomes unparalleled.

AI in cybersecurity sparks both optimism and caution as different perspectives envision its potential benefits and risks. Achieving a balance between the advantages and disadvantages of AI and ML data-protection systems is the goal for the upcoming years. Together, with a focus on innovation and security, we can navigate the future of AI in cybersecurity.

FAQs

1. How does AI enhance threat detection in cybersecurity?

AI enhances threat detection in cybersecurity by using advanced machine learning algorithms to analyze vast amounts of data in real-time. These algorithms can identify patterns and anomalies that may indicate potential threats, enabling organizations to detect and respond to cyber threats quickly and effectively.

1. Can AI-powered cybersecurity solutions adapt to evolving threats?

Yes, AI cybersecurity solutions can adapt to evolving threats. Machine learning models constantly learn from new data and can identify new patterns and behaviors associated with emerging cyber threats. This adaptive capability allows organizations to stay ahead of cybercriminals and continuously improve their cybersecurity defenses.

1. How does AI help in vulnerability assessment and management?

AI can aid vulnerability assessment and management by scanning and analyzing an organization's infrastructure for potential weaknesses and misconfigurations. It can identify vulnerabilities in applications and systems, allowing organizations to take proactive measures to mitigate potential threats before cyber attackers exploit them.

1. Can AI predict future cyber attacks?

AI has predictive capabilities enable it to anticipate potential cyber-attacks based on historical data and patterns. While it cannot predict specific attacks with certainty, AI can identify trends and indicators that may signal future cyber threats, allowing organizations to take preventive measures and strengthen their cybersecurity defenses.