According to Check Point’s latest research report, weekly cyber attacks have increased worldwide by 7% in Q1 2023 compared to the same period last year, with each firm facing an average of 1,248 attacks per week.
The number of cyberattacks is increasing at an alarming rate. According to an IBM study, 68% of cybersecurity responders say they are often responding to multiple incidents simultaneously.
The number of cyberattacks is increasing due to various factors, including the severity of cyberattacks, the growing number of connected devices, and the complexity of business networks resulting from edge computing, the internet of things (IoT), and remote work. This complexity makes it challenging for security teams to see and respond to cyberattacks effectively.
This is where artificial intelligence (AI) comes in. AI is a powerful tool that can be used to improve cybersecurity. It can automate tasks involved in cyber defense, such as threat hunting, incident response, and vulnerability management.
In this blog, we will explore How artificial intelligence (AI) is giving defenders a much-needed edge in the fight against cybercrime.
To learn more about cybersecurity, read our article, “ The Cyber Battle: A shield against digital threats.”
AI-Powered Malware Detection.
AI can play a crucial role in detecting threats like malware. It can analyze user behavior to identify any changes after an employee clicks on a phishing email.
AI can be used to detect this threat in a few ways.
⦁ AI can analyze the user’s behavior to see if it has changed since they clicked on the phishing email. For example, the AI might notice that the user is now opening files from unknown sources, or that they are trying to access systems that they normally do not have access to.
⦁ AI can analyze network traffic to see if there is any unusual activity. For example, the AI might notice that there is a lot of traffic between the user’s computer and a server that is known to be used by threat actors.
⦁ AI can analyze the malware itself to see if it has any known signatures. If the malware does have known signatures, then the AI can alert the security team so that they can take action to remove it.
By leveraging AI’s analytical capabilities, security teams can detect and prevent cyberattacks by examining user behavior, network traffic, and malware.
AI-Driven IT Asset Management for Enhanced Resilience.
AI systems can create detailed IT asset inventories, which include devices, users, and applications within an organization. This inventory can be used to:
⦁ Predict potential attack vectors: By understanding the organization’s IT assets, AI can identify potential attack vectors and allocate resources to vulnerable areas.
⦁ Strengthen resilience to cyber threats: AI analysis can provide suggestions to improve controls and processes, making the organization more resilient to cyber threats.
For example, AI can identify devices that are not properly patched or that are running outdated software. These devices are more likely to be vulnerable to attack, so AI can help to prioritize them for remediation.
AI can also identify users who are accessing sensitive data or who are behaving suspiciously. These users can be flagged for further investigation. By providing this level of insight, AI can help organizations to improve their cybersecurity posture and protect themselves from cyberattacks.
AI-Powered Data Breach Prevention.
Data breaches are a major threat to organizations of all sizes. In 2022, there were over USD 4.35 million records exposed in data breaches, according to IBM. AI can be used to prevent data breaches in a number of ways, including:
⦁ Identifying patterns and anomalies: AI can be used to analyze large amounts of data to identify patterns and anomalies that may indicate a data breach. For example, AI can be used to track user behavior and identify patterns that suggest that a user may be trying to access data that they are not authorized to access.
⦁ Encrypting data: AI can also be used to encrypt data, making it more difficult for unauthorized users to access. Encrypted data can only be decrypted by authorized users, who have the necessary keys.
⦁ Monitoring for threats: AI can also be used to monitor for threats to data security. For example, AI can be used to scan for malware and other malicious software.
⦁ Automating security tasks: AI can also be used to automate security tasks, such as vulnerability scanning and patch management. This can help to reduce the risk of human error, which is a common cause of data breaches.
By using AI, organizations can significantly reduce the risk of data breaches. However, it is important to note that AI and ML are not a silver bullet. They should be used as part of a comprehensive data security strategy that includes other security measures, such as strong passwords, firewalls, and intrusion detection systems.
AI-Driven Uncharacteristic Activity Detection.
AI can identify uncharacteristic actions by analyzing user behavior, network traffic, and system logs. It establishes a baseline of normal activity and flags deviations from the norm. For example, if a user typically logs in from their home office during business hours, but then suddenly starts logging in from a different location at odd hours, this could be a sign of unauthorized access.
AI can also use machine learning algorithms to recognize patterns of uncharacteristic activity, such as identifying phishing emails based on misspellings, poor grammar, and unusual requests for personal information.
AI-Powered Flaw Identification
Artificial intelligence (AI) can be used to identify flaws in a variety of ways. For example, AI can be used to:
⦁ Identify overflow of data in a buffer. This is a common type of vulnerability that can be exploited by attackers to gain access to a system. AI can be used to analyze data patterns to identify potential overflows.
⦁ Locate errors concerning cyber security, bugs, and other flaws. AI can be used to analyze code and identify potential security vulnerabilities. This can help organizations to identify and fix flaws before they are exploited by attackers.
⦁ Identify suspicious information transmitted from a particular application. AI can be used to analyze network traffic to identify suspicious patterns. This can help organizations to identify malware and other threats that are being transmitted over the network.
Machine learning is a type of AI that can be used to identify flaws in a more automated way. Machine learning algorithms can be trained on a dataset of known flaws to learn how to identify new flaws. This can help organizations to identify flaws more quickly and efficiently.
AI-Accelerated Detection and Response.
AI accelerates detection and response times by analyzing large amounts of data quickly, learning and adapting over time, and automating tasks. This enables the identification of threats that might otherwise go unnoticed, improves the effectiveness of response strategies, and frees human analysts to focus on complex tasks.
By harnessing the power of AI, organizations can strengthen their cybersecurity efforts, detect and respond to threats more effectively, prevent data breaches, and identify and fix vulnerabilities in a timely manner. However, it’s important to note that AI should be used as part of a comprehensive cybersecurity strategy and are not standalone solutions.
Conclusion:
In conclusion, AI is a powerful ally in the fight against cybercrime. It can automate tasks, analyze large amounts of data, and detect patterns that humans might miss. By harnessing the analytical capabilities of AI, organizations can detect and respond to threats more effectively, strengthen their resilience, and mitigate vulnerabilities. However, it is important to remember that AI is not a standalone solution, but rather a crucial component of a comprehensive cybersecurity strategy.
