AI-Powered Cybersecurity: Top Use Cases in 2023

AI made its mark on the cybersecurity sector in 2020, with a value exceeding . Experts predict this number will skyrocket to $46 billion by 2027. $10 billion What's driving all this excitement? AI brings some unbeatable firepower. It can crunch massive data sets, spot patterns, and make accurate decisions. These decisions transform how we combat cyber threats. New defensive use cases for AI are emerging. Some of the biggest names in tech—Microsoft, Google, and SentinelOne—have released large language model (LLM)-based products. Big Tech and AI in Cybersecurity In April 2023, Google SEC-PaLM__,__ an AI model designed for cybersecurity. This AI can process threat intelligence data, enabling detection and analytics. introduced Meanwhile, Microsoft Security Copilot deploys GPT4 to analyze threat signals, generating summaries for human review. International payment giant PayPal uses AI to evaluate transactions in real-time; flagging any irregularities. It learns from each transaction, progressively enhancing its accuracy and effectiveness. Similarly, Mastercard's Decision Intelligence relies on artificial intelligence and machine learning to differentiate between legitimate and fraudulent transactions. It prevents fraud without disrupting legitimate transactions, demonstrating AI's ongoing learning and improvement. BAE Systems analyzes network behavior and detects anomalies using sophisticated computer programs. And if anything appears out of the ordinary, it notifies human experts. These niche products highlight how AI can help with security. AI-powered solutions enhance efficiency, open new avenues for growth, and improve users' understanding of active threats. Let’s explore more ways companies are adopting AI in cybersecurity. Top Use Cases for AI in Cybersecurity Advanced Malware Detection We face 10.4 million malware attacks annually, costing businesses $2.5 million. Attacks are becoming increasingly difficult to detect using traditional methods. But AI, via machine learning, identifies and thwarts malicious software like Trojans, viruses, and rootkits. Google, for example, uses machine learning (ML) to identify and assess malware targeting Android devices. Google also scans over 300 billion Gmail attachments every week to ensure their safety. Here's how AI transforms malware detection: Behavioral Analysis: AI algorithms study file behavior to uncover malware-like patterns. Signature-Based and Anomaly Detection: Machine learning identifies new malware signatures, including zero-day attacks. Automated Classification: AI's data analysis capabilities enable it to find patterns and anomalies that might go unnoticed by humans. This makes it excellent at detecting new and unfamiliar malware strains that traditional antivirus software might miss. Streamlining Workflows AI-powered automation simplifies routine tasks, allowing cybersecurity teams to focus on more important matters. AI handles manual compliance tasks. It provides content for standard regulations like HIPAA, GDPR, PCI DSS, and NIST. It also uses rule-based logic to alert compliance personnel about changes and gaps in compliance. It does this by assessing the importance of alerts and then notifying the team. Next, it responds to attacks by isolating the affected systems. This goes on around the clock. Say an employee opens a malware-infested link. AI can recognize malware signatures and take action, preventing further damage. Darktrace is a popular AI-powered cyber defense software. Their AI feature “RESPOND” learns on its own and develops clever ways to fight cyber threats. And the more it learns, the better it gets. User Authentication and Access Control Traditional authentication methods relying on passwords are vulnerable to breaches because of password reuse and stolen credentials. AI offers more robust authentication solutions. It considers factors like biometrics and user behavior patterns. AI-based systems use behavioral biometrics, device fingerprinting, and contextual information for multifactor authentication. They are making it difficult for unauthorized users to gain access, even with stolen credentials. Different types of AI authentication include: Biometric Authentication: AI plays a significant role here, with examples like keystroke dynamics, behavioral biometrics, facial recognition, and voice recognition. Behavioral Biometrics: This method is becoming popular due to its continuous authentication capabilities. It tracks and verifies user behavior patterns, providing a secure and password-free authentication experience. Fighting Cybercrime Cybercrime steals about of the world's GDP. A while ago, two-factor authentication was the best way to keep things secure. Now, AI is adding more layers of protection by using data to verify identities. 1% As cyber threats evolve, firewalls and antivirus software are no longer enough. You need more intelligent tools like XDR and managed SOC/SIEM. These tools help to outpace and outsmart threats. Deep learning enables AI to stay on top of cybercrime. It searches logs, real-time messages, and transactions for anything suspicious. AI in Advanced Threat Prevention AI's real-time advanced threat detection has saved businesses Advanced threats, sometimes called advanced persistent threats (APTs), are cyberattacks that target a specific organization over a long period of time. $3.05 million. They create new ways to get in and use tools to cause severe damage. Skilled attackers with plenty of resources often launch these attacks, and they can stay hidden in a system for weeks or months. Businesses use AI to examine network traffic, user behavior, and logs to detect potential threats and act before issues escalate. Ways AI helps in advanced threat prevention: Deep Learning for Advanced Threats: AI uses deep learning to identify sophisticated threats. They can detect APTs, insider threats, and evasive attacks that traditional security measures miss. Tackling False Positives: Because they are constantly learning and improving, machine learning algorithms can distinguish legitimate activities from potential threats, reducing false positives. Enhancing Threat Intelligence: AI algorithms link known indicators of compromise (IoCs) with internal security data by analyzing threat intelligence feeds. It strengthens defenses against new threats before they happen. Scalability and Efficiency: AI-driven threat detection processes massive amounts of real-time data to uncover threats across networks. This cuts down on the amount of manual analysis and speeds up detection and response. Outsmarting Phishing Traditional phishing detection methods relied on fixed rules and lists to catch well-known tricks. But they fail against newer schemes. This is where AI-driven phishing detectors come in. Using machine learning, they dive into emails and look for signs of phishing. The algorithms learn from massive data pools to identify phishing patterns. They can even track user actions in emails. And alert the security team if someone clicks a dodgy link or gives out personal information. Endpoint Security Cybercriminals often go after endpoints like laptops and smartphones. Traditional antivirus software relies on known malware signatures. But AI looks at how malware acts to find even unknown variants. CrowdStrike's Falcon harnesses the power of AI to protect user endpoints. Falcon uses machine learning algorithms to spot potential threats even when devices are offline. Here's how it works: Scan and Isolate: AI-based security scans for malware and isolates suspicious files. Behavior Monitoring: It tracks device actions. At the same time, keep an eye out for anything unusual that could be a security threat. Blocking Access: These AI tools block unauthorized attempts. And stop attackers from grabbing sensitive data. A significant benefit of AI-based endpoint security is its ability to adapt. Takeaway As cyber threats worsen, human efforts alone are no longer enough. AI helps cybersecurity experts reduce breach risks and strengthen security by analyzing and detecting threats. While AI powered cybersecurity applications might have risks, partnerships between humans and machines create a safer, better future.