Tech experts say that AI won’t replace humans any time soon. This is true. However, AI is empowering humans to automate easy and voluminous tasks, allowing them to utilize their time and focus on something more concrete to their cybersecurity strategies.
AI tools are being widely used in the cybersecurity field for filtering out large amounts of data with speed and accuracy, and not to forget how they help get rid of human errors. These tools can practically work around the clock without getting tired, enabling humans to focus only on dealing with anomalies and vulnerabilities.
Considering the upside of AI’s contribution to human efforts, Google has also developed and introduced some complementary cybersecurity tools driven by artificial intelligence and machine learning.
AI Enhances Human Capabilities
Automated data filtration, vulnerability assessment, penetration testing, attack surface monitoring and reporting, pattern identification, logs and data reviewing, etc., saves time and boosts accuracy. Moreover, some menaces go past the human eyes, which isn’t the case with AI. That’s why threats are detected at an early stage, making it easier for cybersecurity personnel to fix or eradicate them.
Assessing and fixing mature security loopholes is complicated due to the intricacies involved and their widespread nature. Once a system has been infiltrated, CISOs need to move much faster than the adversaries to mitigate further exploitation.
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6 Uses of AI in Cybersecurity
A mid-size company encounters over 200,000 alerts for cyber events every day. It’s not practically possible for humans to catch, address, and fix them manually. So, here are a few ways in which cybersecurity personnel are already leveraging AI’s capabilities–
1. Security Screening
Immigration officers use AI to catch people lying about their intentions by picking up minor variations of facial expressions and body language. The system features a display showcasing a digital interface with an animated face that questions passengers and observes variations in responses and voice tone.
The gathered information undergoes scrutiny against indicators associated with potential deception. Individuals deemed suspicious are identified for additional scrutiny during the screening process.
2. Mobile Endpoints Analysis
Mobile endpoint analysis using AI employs machine learning algorithms to make mobile devices safe. AI performs behavioral analysis by understanding normal device and user behavior, enabling the detection of anomalies and potential threats. Machine learning models are utilized for malware detection, identifying both known and previously unseen threats based on behavior patterns.
3. Threat Detection and Response
AI-based tools gather data from various sources, including network logs, endpoint activities, user behavior, and external threat intelligence feeds to catch and highlight anomalies, suspicious behavior and requests, malware detection, pattern recognition, etc.
The AI-based automated response mechanisms execute predefined actions or mitigation strategies, such as isolating compromised devices or blocking suspicious network traffic.
4. Detecting Sophisticated Cyberattacks
AI plays a crucial role in reducing threat response time in cybersecurity, as automated detection allows for the immediate identification of potential threats without human intervention.
AI is smart enough to predict potential security threats based on historical data and emerging trends. This enables security teams to take preventive measures before a threat actor exploits a vulnerability, reducing the overall response time.
5. Aiding Intrusion Detection and Prevention
Performing intrusion detection and prevention using AI helps cover the maximum attack surface while gathering data from multiple sources and improving overall situational awareness.
We can’t also deny that AI-driven intrusion detection systems can automate tasks such as patching and configuration changes so that problems can be identified as soon as possible and there’s enough time to respond to potential threats without jeopardizing operations. This automation expedites security responses and leads to cost savings by reducing administrative overhead expenses associated with manual processes.
6. Efficient Vulnerability Assessment and Penetration Testing
AI-driven vulnerability assessment and penetration testing tools identify vulnerabilities that might escape human or traditional scanner detection. These tools are trained to navigate web traffic to a designated AI model to do a seamless and advanced analysis when the scanning is done. Additionally, the prompts can be modified, which empowers cybersecurity team members to customize web traffic analysis according to their specific requirements.
On top of these AI-driven capabilities, email security and email authentication play a crucial role in maintaining a strong cybersecurity stature. Incorporating measures like advanced spam filters, phishing detection, and two-factor authentication can significantly bolster the security of email communications. By validating the reliability of incoming emails and safeguarding sensitive information contained in outgoing emails, organizations can further reduce potential cybersecurity threats and safeguard their data.
