If a cyberattack meets the criteria defined by a security staff, AI can create automatic responses and isolate the affected data. Generative AI takes one more step and produces original natural language scripts, visuals, and other content-based data.
How does it work?
AI for cybersecurity works by assessing huge amounts of data across various sources to identify activity across an organization, including the time and place where people log in, traffic movements, and the devices and cloud systems that individuals use.
Once it understands what the pattern is, it can recognize unusual behavior that may need to be analyzed and targeted. To keep safe, an organization’s data is not used for any AI output processes at other organizations. Instead, AI uses worldwide threat intelligence data taken from various multiple organizations. It uses machine learning algorithms to be able to learn based on the data of the systems.
When generative AI identifies specific cyber threats, such as malware, it can help to find and fulfill its context threat analysis and make it easier to teach users by paraphrasing texts or images to define what is going on. However, the importance of people in cybersecurity cannot be disregarded, but AI increases their abilities and helps identify and overcome threats faster.
How can AI protect?
Data protection
Security crews use AI to identify and mark sensitive information throughout the environment, whether it is hosted on the infrastructure of a database or stored in a cloud application. AI can also help discover when a cyberattack is targeted to transfer data from the company and either prevent the action or notify the security team.
Incident investigation and response
During incident response, the security team must sort through a great deal of data to reveal possible cyberattacks. AI helps identify and present the most effective events across various data sources; it saves a huge amount of time. Generative AI drives investigation even further by translating analysis and answering questions in natural language.
Detection and prevention
One of the most critical uses of AI for cybersecurity is detecting and preventing cyber threats. There are certain methods that AI help recognize and block cyberattacks:
- Learning models use labeled and categorized data to train a system. To illustrate, certain known malware has exclusive signatures that differentiate it from other sorts of cyberattacks.
- While unsupervised learning, machine learning algorithms identify patterns in data that have not been yet labeled. This is how AI detects advanced or appearing cyberattacks that do not have known signatures. They follow activity that falls outside the standard, or they track patterns that copy previous cyberattacks.
- These systems analyze user normal traffic patterns to understand usual behavior to detect any unexpected or suspicious activities that might indicate an account compromise.
Cybersecurity AI Tools
AI has been working with various cybersecurity tools to improve their quality. Here are some examples:
- Next-Generation Firewalls: It is a security appliance that progresses network movements and tracks rules to prevent unusual traffic. NGFWs evolve and reach beyond the limits of traditional firewalls. They work in the same way firewalls do, however, in a more powerful way.
- AI-Enhanced Endpoint Security: AI helps in issue response by promptly synthesizing attacks, recommending cure steps, and creating automatic responses to annihilate damage. It was developed to detect phishing and malware through machine learning algorithms that analyze email content, the attitude of the sender, and software features to identify and block possible threats.
- AI-driven network Intrusion Detection and Prevention: AI empowers violation detection systems with adaptive defensive mechanisms. The system can autonomously set its response strategies based on the context of detected threats, developing its ability to work against sophisticated attacks.
Here are some examples of AI-powered cybersecurity tools:
Next-Generation Firewalls:
- Palo Alto Networks (Cortex XDR): Integrates AI to enhance threat detection and response capabilities.
- Fortinet (FortiGate): Uses AI to improve threat intelligence and block advanced threats.
AI-Enhanced Endpoint Security:
- CrowdStrike (Falcon): Employs AI to detect and prevent malware and other endpoint threats.
- SentinelOne: Utilizes AI for real-time endpoint protection, threat detection, and response.
AI-Driven Network Intrusion Prevention:
- Darktrace: Leverages AI to detect and respond to network intrusions by analyzing network traffic patterns.
- Cisco (Secure Network Analytics): Uses AI to identify and respond to threats within the network.
AI and Cloud Security:
- Microsoft Azure Sentinel: A cloud-native SIEM that uses AI to provide security analytics and threat intelligence.
- Google Chronicle: Offers AI-driven insights to identify threats across cloud environments.
Securing IoT Devices with AI:
- Armis: An AI-based security platform that provides visibility and protection for IoT devices.
- Zingbox: Uses AI to secure IoT devices by identifying and responding to threats.
XDR and SIEM Solutions:
- Splunk (Enterprise Security): A SIEM platform that integrates AI for advanced threat detection and response.
- IBM QRadar: Utilizes AI to correlate data from various sources for more effective threat detection and investigation.
Staying secure means staying smart. Leverage the power of AI to protect your business from advanced threats, safeguard your network, and ensure the safety of your endpoints and IoT devices. Ready to enhance your cybersecurity strategy? Explore our AI-driven solutions today and see how we can help you secure your future.
Contact us now to learn more or schedule a demo with one of our cybersecurity experts!