Approximately 89.7% of organizations experienced at least one cyber attack in 2023. This surge in digital threats is underscored by the staggering $8.64 million average cost of security breaches, positioning the US at the forefront of global security breach costs.
As cyber threats continue to disrupt businesses, we need solutions that effectively manage the risk environment in the digital era. The emergence of artificial intelligence (AI) offers much promise.
In this article, we’ll share nine applications of AI to protect your organization from both rare and common cyber threats.
1. Anomaly detection
AI is well-equipped to analyze vast amounts of data to establish normal behavior patterns.
AI algorithms can then quickly identify any deviations in the norm that could indicate a cyber threat, notifying the organization who can respond.
Traditional rule-based systems are limited in detecting new and evolving threats. AI uses machine learning algorithms that learn and adapt without needing explicit instructions.
The algorithm analyzes historical data to establish a baseline of normal behavior. This process never stops. The algorithm continuously uses incoming data to update its learning and form new baselines.
Once an anomaly is detected, the AI system can trigger an alert or take immediate action to mitigate the risk. This could involve blocking suspicious network traffic, quarantining compromised devices, or notifying human intelligence for further investigation.
The algorithm learns from malicious activities to continuously improve its detection capabilities, giving it the power to highlight existing security flaws.
Anomaly detection isn’t foolproof and can sometimes generate false positives or negatives.
- False positives occur when the system flags legitimate activities as anomalies.
- False negatives are when the system fails to detect actual threats.
To address these challenges, cybersecurity experts continuously refine and fine-tune AI algorithms.
How? By incorporating feedback from security analysts and incorporating new threat intelligence. This iterative process helps to reduce false positives and increase overall accuracy.
2. Predictive analysis
The data analysis performed by AI’s machine learning algorithms can nip threats in the bud. But it also identifies the likelihood of future cyber attacks and potential security breaches — known as predictive analytics — through analyzing real-time data.
Organizations can use this data to take preemptive measures and implement robust defense mechanisms before an attack occurs.
This is particularly helpful to financial institutions and e-commerce platforms, as it can prevent financial losses and protect customers from fraud.
To minimize risk to your server infrastructure, consider running your data analytics on bare metal cloud; thanks to dedicated resources, you’ll get enhanced security, no noisy neighbors, and no hardware overbooking.
3. Phishing detection
Phishing is a fraudulent attempt to trick individuals into sharing sensitive information. Bad actors target usernames, passwords, and credit card details by disguising as a trustworthy entity in electronic communication, typically an email.
The goal of phishing is to steal sensitive information or to install malware on the victim’s device.
AI-enhanced phishing detection goes beyond simple rule-based systems, employing sophisticated algorithms to analyze various aspects of emails for signs of phishing.
Key elements include:
- Sender information scrutiny: Beyond the content, AI also examines the sender’s information. It checks the sender’s domain for authenticity and looks for spoofing signs where attackers might impersonate a legitimate entity. AI algorithms can compare the sender’s information against known phishing sources and verify if the email aligns with the sender’s typical communication patterns. You can also utilise an email virtual assistant for the same.
- Content analysis: AI systems scrutinize the email’s text for phishing indicators. It looks for suspicious links, deceptive language patterns often used in phishing emails, and other textual anomalies that might not be obvious to the naked eye. By learning from vast datasets of known phishing emails, the AI becomes adept at spotting subtle cues that signal a phishing attempt.
- Contextual analysis: AI systems can analyze when emails are sent, how often they come from the same sender, and if the content is relevant to the recipient. By understanding the broader context, AI can identify phishing attempts that might otherwise seem legitimate in isolation.
4. Malware detection and analysis
Malware is a type of software that aims to harm a computer system, network, or device. It’s an umbrella term that includes various types of malicious software such as:
- Ransomware
- Spyware
- Viruses
- Adware
- Trojans
- Worms
Malware can steal sensitive information, monitor user activity, damage files, and disrupt normal computer operations. How? It spreads through email attachments, software downloads, social engineering tactics, and vulnerabilities in software or operating systems.
Thankfully, AI algorithms can dissect and understand new malware strains through a combination of code analysis, behavioral assessment, and examination of other defining attributes.
Here’s how it works:
- Code analysis: AI algorithms scrutinize the code of software or files to detect malicious patterns. Traditional antivirus software uses known malware signatures to detect threats. But with AI, new or changed malware can be identified by analyzing code structures and patterns that show malicious intent. This is particularly effective against polymorphic and metamorphic malware, which change their code to evade detection.
- Behavioral analysis: Beyond analyzing code, AI systems monitor how programs and files behave in a system. This includes tracking file executions, network activities, and system changes. After establishing a baseline of normal behavior, AI can flag activities that deviate and indicate malware. This approach is effective in detecting zero-day attacks, where the malware is previously unknown.
- Rapid development of countermeasures: Upon identifying a new malware strain, AI systems can assist in rapidly developing effective countermeasures. For instance, it can devise strategies to neutralize the threat, patch vulnerabilities, and disperse updated defense mechanisms to protect against similar attacks in the future.
- Attribute assessment: AI can assess various attributes of a file or program, such as its origin, metadata, and the manner of its distribution. This holistic view helps in identifying the likelihood of a file being malicious.
5. AI and intrusion detection systems (IDS)
Using artificial intelligence, an intrusion detection system can analyze large quantities of network information in real-time. That way, it can quickly detect any irregularities or patterns that could signal unauthorized entry or security breaches.
It employs machine learning to constantly update its knowledge of typical network activity.
This approach allows AI-enhanced IDS to recognize even subtle, previously unseen attack patterns, making them highly effective against zero-day exploits and advanced persistent threats.
6. Password management and security
Password management for businesses is a crucial aspect of cybersecurity, as it helps protect sensitive data and credentials from unauthorized access.
However, managing strong, unique passwords can be challenging — especially when dealing with multiple devices, platforms, and users.
AI helps automate password creation, storage, and rotation, detecting and preventing phishing attacks, password breaches, and credential theft.
For example, a password manager that uses AI to generate strong and unique passwords for each account can sync them across devices and alert users of suspicious activity using those passwords.
Screenshot by author
7. AI in security policy management
With the proliferation of devices, networks, and user accounts, the task of managing security policies manually is challenging and prone to errors. AI offers a solution to this by bringing automation to the table.
For instance, consider the dynamic nature of user roles and permissions in a large organization. AI can track these changes and automatically update access controls based on the user’s current role, ensuring adherence to the principle of least privilege.
Additionally, AI’s ability to understand context adds a layer of sophistication to security policy enforcement. If an employee’s access pattern changes anomalously, AI can introduce additional authentication steps or temporary restrictions to mitigate any security risks.
On the vulnerability management front, AI excels in identifying and suggesting remediations for security weaknesses within network configurations.
It can pinpoint outdated software and propose updates, keeping the system in line with the latest security standards. This proactive stance is crucial for maintaining a robust defense against emerging cyber threats.
AI also plays a crucial role in ensuring compliance with various regulatory standards. By parsing through the complex web of regulations, AI can help organizations adjust their policies to stay compliant with standards like GDPR, HIPAA, or PCI DSS.
8. AI and unauthorized database access
Another way AI can improve cybersecurity is by helping to secure authentication protocols like OAuth and SAML to ensure data privacy as teams increasingly work remotely.
These protocols allow users to log in to applications without sharing their passwords, but they can still be vulnerable to cyberattacks.
AI can analyze login data for suspicious activity, such as attempts to log in from unusual locations or using stolen credentials. Now, you can prevent cyberattacks before they happen.
For instance, OAuth, which is commonly used in web applications, relies on third-party providers like Google, Apple, or Facebook to verify user identities. While this can be convenient for users, it also introduces the risk of data breaches at the third-party provider.
Screenshot provided by the author
AI can monitor OAuth logins for anomalies, such as a sudden surge in login attempts from a particular location or IP address. This scenario might indicate a phishing attack or other malicious activity.
Similarly, SAML, which is often used in enterprise environments, relies on a central identity provider to verify users.
AI can analyze SAML login data for suspicious activity, such as attempts to access unauthorized resources or use stolen credentials. This can help to prevent data breaches and other security incidents.
9. Leveraging CNAPP for enhanced cybersecurity
Cloud-native application protection platforms (CNAPPs) are comprehensive security solutions that consolidate various cloud security capabilities, thereby protecting cloud environments against cyberattacks.
Screenshot by author
They offer a unified platform encompassing Cloud Security Posture Management, Cloud Workload Protection, and more, providing a holistic defense against complex security challenges in cloud-native applications.
This integration of AI with CNAPPs can significantly bolster an organization’s ability to detect and prevent cyber threats.
Wrapping up
AI offers many powerful capabilities to detect and prevent cyberattacks.
The crux of AI is its ability to learn on its own using the data it’s given, making it a powerful ally for organizations as well as a massive upgrade on existing threat detection methods.
In 2024, investing in AI security with human oversight will significantly secure businesses against cyber-attack attempts, showing no signs of slowing down.
Author bio
Arif Bharakda is a versatile writer passionate about marketing, technology, and B2B insights. With a keen interest in personal growth, Arif brings a relatable perspective to complex topics, making them accessible to all readers.
