As technology evolves, so do the cyber threats from hackers and attackers. According to the FBI, internet-related crimes rose sharply with over 847,000 complaints and nearly $7 billion in losses, marking a considerable jump from the prior year.
Cybercrimes like phishing, scams, and data breaches put organizations in jeopardy. To tackle these dangers, companies are employing qualified cybersecurity teams with innovative technologies, including artificial intelligence (AI) that quickly spots and counters malicious activities, fortifying systems against threats.
The potential of AI has led 76% of companies to prioritize AI and machine learning in their IT budgets, driven by the massive amount of data requiring analysis to effectively identify and combat cyber threats.
With connected devices expected to generate a staggering 79 zettabytes of data by 2025, manual human analysis becomes impractical, making AI an essential tool against cybercrime.
This article explores the vital role of artificial intelligence in cybersecurity, its advantages, challenges, and real-world use cases from major global companies like AWS and PayPal.
The Significance of AI in Cybersecurity
The market for AI in cybersecurity is growing at a rapid pace, as per Verified Market Research. They estimate that the market was valued at $17 billion in 2022, and is projected to reach $102 billion by 2032. This substantial growth highlights the escalating need for AI solutions, as hackers are also leveraging advanced technologies for malicious activities.
With the rising frequency of cyberattacks globally, there is heightened attention on using AI for cybersecurity. A survey by The Economist Intelligence Unit found that almost half of business executives and security experts see AI and machine learning as effective tools to tackle modern cyber threats. Additionally, Pillsbury’s report showed that 44% of organizations worldwide already use AI to detect security breaches.
AI establishes secure-by-default applications, eliminating vulnerabilities for users. By removing negative defaults, AI enables accurate detection of issues, faster investigations, and automated response systems. AI-powered solutions like behavioral biometrics for user verification can enable secure app development and a safe data environment, contributing to robust infrastructure.
AI can identify potential threats and bad actors, helping organizations predict and prevent attacks before they happen. With AI-enabled continuous monitoring, systems can stay protected round the clock, allowing proactive measures to safeguard digital assets before any damage is done.
What are the practical uses of artificial intelligence in the field of cybersecurity?
Artificial intelligence has several uses in the field of cybersecurity. Some of the key applications include predicting the risk of data breaches, detecting phishing attempts, identifying malware and preventing its spread, authenticating users, filtering spam emails, safeguarding passwords, spotting bots, analyzing behavior for anomalies, segmenting networks and securing them, detecting fraud, gathering intelligence on threats, responding to security incidents, managing vulnerabilities, and managing identity access.
In summary, AI can be leveraged in cybersecurity for threat prediction and prevention, user and data protection, network security, detection of attacks and fraud, threat intelligence, and incident response. The goal is to utilize AI’s capabilities like machine learning to automate security processes and enhance cyber defenses.
Artificial Intelligence Applications in Cyber Security
Artificial intelligence is gaining widespread adoption in cybersecurity budgets and strategies, with 76% of companies prioritizing AI and machine learning investments according to Forbes. There are several benefits to integrating AI into cybersecurity efforts:
Threat Recognition and Prevention
AI is especially talented at recognizing threats. It can review immense amounts of data from multiple sources and pinpoint uncommon patterns in user actions that may indicate a cyberattack. For instance, if an employee inadvertently clicks on a phishing email, AI can rapidly detect the change in their behavior and warn us of a potential security breach.
When a possible threat is identified, AI-fueled systems activate real-time alerts and notifications to security teams, allowing quick and effective responses. By automating incident response measures like isolating impacted systems or blocking malicious activities, AI minimizes opportunities for attackers and restricts the potential impact of a security breach.
- Phishing and Malware Identification
AI-powered cybersecurity systems demonstrate enhanced effectiveness. Chuck Everette of Deep Instinct reveals that AI systems have security rates of 80% to 92%, exceeding the 30% to 60% achieved by legacy signature-based malware detection systems.
AI systems excel at detecting phishing traps, thereby thwarting potential threats. Researchers from the University of North Dakota proposed a machine learning-based phishing detection technique with an impressive 94% accuracy in classifying emails as legitimate or phishing.
- Security Log Review
AI transforms security log analysis by leveraging machine learning algorithms to analyze massive amounts of real-time log data. By detecting patterns and anomalies without known threat signatures, AI empowers organizations to identify and respond to potential security breaches swiftly.
- Endpoint Protection
With the increasing prevalence of remote work, it is crucial to prioritize the security of endpoints to maintain strong cybersecurity. AI-powered endpoint protection adopts a proactive strategy by establishing baseline patterns of normal endpoint behavior and identifying any deviations in real time.
Through continuous learning from network activity, AI has the ability to recognize potential threats, such as zero-day attacks, without relying on signature updates. With the integration of AI, businesses can improve password security and user account protection by implementing advanced authentication methods such as CAPTCHA, facial recognition, and fingerprint scanners to automatically verify genuine login attempts.
- Encryption
AI struggles to break modern encryption like AES and SHA which rely on complex math tricks that make prediction difficult. While AI can do amazing things, strong encryption remains a big challenge to crack.
- Threat Detection in Honeywell
With AI, Honeywell’s platform can rapidly analyze huge industrial control system datasets, identifying any abnormal patterns or behaviors indicating a cyber threat.
Additionally, 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 significant damage occurs.
Analysis of user behavior
AI models utilize sophisticated machine learning techniques to constantly analyze network activity and detect irregularities compared to normal patterns. Over time, these models improve themselves and adapt, enhancing their precision in pinpointing anomalies and potential threats. The self-improving nature of AI models provides organizations with strong and dependable cybersecurity defense systems that can respond rapidly to emerging cyber threats.
AI-powered behavioral analytics boosts threat-hunting efforts by building profiles of deployed applications and examining vast user and device information. This proactive methodology enables organizations to effectively identify evolving dangers and vulnerabilities.
- User Behavior Analytics on Amazon Web Services
Through their Amazon Web Services (AWS) platform, Amazon provides various AI-powered security services that have transformed how businesses approach threat identification and prevention.
- One such service is AWS GuardDuty, a managed threat detection system that analyzes various data sources like AWS CloudTrail logs, VPC Flow Logs, and DNS logs to detect any abnormal behavior that may signal a security breach. This includes spotting unusual spikes in API calls, atypical network traffic patterns, and unauthorized access attempts on sensitive data.
- Another valuable AI-powered service from AWS is AWS Inspector, which assists in identifying security vulnerabilities within an organization’s AWS infrastructure through continuous monitoring.
- Furthermore, AWS Macie, an additional innovative offering, is a fully managed data security service that leverages machine learning to discover, classify, and protect sensitive data within an AWS environment. Macie performs comprehensive data analysis, enabling it to identify critical information such as personally identifiable information (PII), financial data, and intellectual property (IP).
Advanced methods for responding to and mitigating threats
Artificial intelligence systems can automate cybersecurity responses beyond just detecting threats. Organizations can use AI solutions to lighten the workload for security teams and speed up response times to incidents.
By examining huge amounts of security data and finding connections, AI can independently generate informed responses to cyber threats that align with technical logs, network patterns, and global threat intelligence.
At the heart of Wells Fargo’s cybersecurity approach is an AI platform for finding and responding to threats. This platform uses advanced machine learning to analyze massive amounts of data, including network activity, emails, and files. By processing this data in real time, the AI can spot patterns and anomalies that may signal malicious actions.
Once the system detects a potential threat, Wells Fargo’s AI can automatically trigger proactive defenses. For example, it can quickly block harmful traffic or isolate infected files to stop the threat from spreading further on the company’s network.
Evaluation and Control of Vulnerabilities
As cyber criminals continue to use more advanced techniques, organizations are struggling to manage the large number of new vulnerabilities that emerge. Artificial intelligence (AI) powered solutions, like User and Entity Behavior Analytics (UEBA), analyze device, server, and user activities to detect anomalies and zero-day attacks. By proactively protecting against undisclosed vulnerabilities, AI enables real-time defense against high-risk threats.
- Vulnerability Assessment and Management with Splunk
The Splunk Enterprise Security platform uses machine learning algorithms to analyze huge amounts of data from various sources, including network logs, system events, and user activity. This AI-driven approach allows the platform to identify patterns and anomalies that could indicate potential vulnerabilities or malicious activities in real-time.
One of the key benefits of Splunk’s AI-powered Vulnerability Assessment and Management is its ability to intelligently prioritize threats. By examining data with AI algorithms, the platform can accurately evaluate the severity and impact of each vulnerability. This enables security teams to focus their efforts on addressing the most critical risks quickly.
Cybersecurity and Automation
A smart cybersecurity system powered by artificial intelligence examines billions of requests, actions, behaviors, and data points across networks and devices daily. This real-time review allows prompt response within minutes, instead of the hours or days it would take with manual methods. IBM found AI can cut the time to identify and react to cyber threats by about 14 weeks.
AI can independently check systems and networks for vulnerabilities, simplifying the identification of potential entry points for hackers. By prioritizing required security updates, AI reduces manual work and decreases vulnerability exposure.
For instance, IBM’s managed security team used these AI abilities to automate 70% of alert resolutions and accelerate threat management timelines by over 50% in the first year.
AI streamlines security operations, optimizing threat detection and removal processes. Its involvement decreases response time, minimizing the risk of human errors in critical tasks. This automation enables cybersecurity professionals to concentrate on strategic choices and improving defenses.
- Security and Automation at Plaid
Plaid uses advanced machine learning to analyze many datapoints, including the customer’s name, address, Social Security number, and more. The AI can accurately and seamlessly identify and verify bank accounts in seconds, reducing errors or fraud. Now it can simplify onboarding for financial institutions and customers. Plaid’s AI-powered platform eliminates manual intervention and paperwork, speeding up customer onboarding while enhancing data security.
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Intelligence on potential threats and the use of predictive analytics
Artificial intelligence systems significantly enhance the ability to predict cybersecurity breaches by furnishing comprehensive inventories of IT assets. These inventories contain all devices, users, and applications with varying levels of access to critical systems. Combining asset inventory information with evaluations of threat exposure enables AI to forecast the areas most vulnerable to cyberattacks.
With AI’s capacity to gather and analyze diverse data sources, security teams gain a holistic perspective of the organization’s security position. This enhanced situational understanding allows proactive threat hunting, precise risk assessments, and timely incident response, strengthening the overall cybersecurity strategy of the organization.
One of the key uses of AI in PayPal’s cybersecurity approach is analyzing transactions. Given the immense volume of daily transactions on the platform, manually scrutinizing for signs of fraud would be extremely difficult. Here, AI’s rapid processing abilities are advantageous as it efficiently examines each transaction for potential red flags.
Furthermore, PayPal expands its vigilance to identifying and blocking malicious websites. As cybercriminals continuously devise new tactics to deceive users through phishing and scam websites, the system diligently scans websites to detect malicious content or indications of potential cyber threats.
The risks and challenges of AI in cybersecurity
AI-based solutions have emerged as powerful tools for businesses, providing critical insights, assisting decision-making, and automating mundane tasks. However, Gaurav Keerthi, Deputy CEO of Singapore’s Cyber Security Agency, cautions that “AI can be a blessing or a curse from a cybersecurity perspective.”
Deploying AI in cybersecurity poses multiple challenges:
| Data manipulation | Hackers can access and distort training data, introducing biases that undermine model effectiveness. They can also alter data to benefit themselves. |
| AI-powered cyberattacks | Hackers can leverage AI to create intelligent malware that modifies itself to evade even the most sophisticated defenses. |
| Insufficient data | AI depends on large volumes of high-quality training data. Without it, models yield inaccurate results and false confidence, letting threats slip by unnoticed. |
| Privacy issues | To discern user patterns, AI models require real user data. But without proper safeguards, this exposes sensitive information to privacy and security risks. |
| Attacks on AI | Like any software, AI systems are hackable. Adversaries can poison data to manipulate AI behavior for malicious ends. |
While concerns exist, the solution is building robust protections into AI infrastructure, not abandoning AI altogether. With the right precautions, AI can provide major security advantages.
Final Thoughts
The use of artificial intelligence (AI) for cybersecurity has both pros and cons. On the positive side, AI can improve the analysis, comprehension, and prevention of cybercrime. This enhances trust and safety for businesses and customers. However, AI can require extensive resources and not always be practical.
Also, cybercriminals can exploit AI to enhance their attacks. One industry benefiting from AI is virtual private networks (VPNs). Machine learning allows VPNs to protect users from online threats posed by AI. The ability of AI to rapidly analyze data has been a discussion topic for some time. This was highlighted two years ago when we examined how AI and machine learning would influence the future of cybersecurity.
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