Machine learning and automation can make a big difference when giving cybersecurity professionals the right tools they need to take action in a timely manner.
The majority of companies, especially those operating in the online space, handle large amounts of data that is too much for humans to process and protect within a limited time frame.
Additionally, the lack of automatic contextualization at scale requires manual follow-up to take corrective action. For example, there is no AI in cybersecurity, so SOCs rely on management teams to block malicious connections, IT support teams to isolate hosts, and email teams to remove hacked mailboxes. Masu.
In the current state of multi-cloud solutions, enterprises leverage disparate security toolsets and traditional data centers, which requires AI cybersecurity solutions to enhance automation. A solution that pulls information from all these diverse platforms to give you a comprehensive view of your organization’s security outlook.
As the graph shows, artificial intelligence in cybersecurity is a must-have for SOC analysts today, as it increases threat visibility across cloud and on-premises environments and ultimately allows for better investigation of compliance-based risks. It has become.
The needs of this era are defining new roles for AI in cybersecurity, both at a holistic and SOC-specific level. Now let’s take a closer look at the use of AI in cybersecurity.
How AI impacts cybersecurity
Machine learning and AI have become essential to security because they can analyze millions of data and track numerous cyber threats. What this technology does best is learn from past data and continually improve. Let’s take a look at some of the benefits of using AI for cybersecurity.
Automate repetitive tasks
AI-based cybersecurity eliminates the need to continually update and deploy security software, learn security skills, or manually back up data. AI-powered cybersecurity platforms enable businesses to perform tasks like compliance monitoring and incident response on autopilot. This level of automation offers a variety of benefits to businesses.
- correlation data
- Build protection against threats quickly
- Detection of infection in the system
Monitor, identify and respond to cyber threats
Use cases for AI in cybersecurity can be seen in techniques that analyze user behavior and infer patterns that identify unusual deviations in business. This allows you to detect vulnerable areas within your system and quickly remediate them to prevent future attacks. Additionally, machine learning is trained on a large number of malware events to proactively detect and predict malware that may enter your IT network.
Track user behavior and activity
Understanding how business users and employees work is necessary to identify and mitigate anomalous behaviors that can be harmful. By using her AI for cybersecurity, SOC analysts can perform multiple actions such as tracking the daily behavior and activities of users on business networks. The AI system then understands behavioral patterns and detects anomalies, highlighting malicious files, infected hosts, and compromised user accounts.
fight bot
Bots can pose a real threat, as they make up a major portion of internet traffic. If left unmanaged, it can lead to account takeover and data fraud, which cannot be addressed with manual action alone. Incorporating artificial intelligence into cybersecurity will allow companies to gain detailed insight into their website traffic and differentiate between good bots, bad bots, and humans.
Predict risk of breach
AI cybersecurity solutions help determine IT inventory, which is an accurate record of users, devices, and applications with multiple access levels. Considering asset inventory and threat exposure, AI-powered cybersecurity predicts the likelihood that business systems will be compromised, leading to timely planning and allocation of resources for addressable vulnerabilities.
landscape analysis
Remote work has become the new normal, making updating legacy systems and building hybrid networks and platforms a business necessity. Employees who use cloud-based apps for work are expanding their business security practices beyond the typical “four walls” of a company. Powering this multi-location work environment requires endpoint security resources to manage transactions, communications, applications, and connectivity.
The use case for AI in cybersecurity in this context is for SOC analysts to use this technology to create correlations between anticipated threats while supporting, reaching, and scaling across these endpoints. and understand how threats affect other resources.
Incident detection and response
Another set of AI in the cybersecurity example can be seen in the technology’s ability to differentiate and prioritize different types of threats and deliver notifications accordingly. This can take many forms, from automating ticket creation and adding relevant remediation information to detecting the presence of malware even before a malicious file or email is opened.
AI-based cybersecurity software for incident detection and response not only reduces dwell time and remediation time, but also provides businesses with the ability to take proactive and pre-emptive action.
To assess the role of AI in cybersecurity, it is important to examine real-world examples of projects centered around the implementation of artificial intelligence in cybersecurity.
Real-world examples of using AI for cybersecurity
The set of AI use cases in cybersecurity described above have multiple real-world examples supporting them. Next, let’s take a look at the top ones.
- Google uses AI to analyze mobile endpoint threats and protect the growing number of mobile devices. MobileIron and Zimperium also announced a partnership to help enterprises deploy AI-based mobile anti-malware solutions.
- Cognito collects and stores network metadata and builds on its own security insights. This insight is used to detect and prioritize attacks in real-time.
- Another example of AI in cybersecurity can be seen with the Darktrace Enterprise Immune System. Based on AI and ML, it models the behavior of all users, devices, and networks to study specific patterns, automatically identifying anomalous behavior, and alerting businesses in real-time.
One of the biggest challenges facing SOCs today is detecting and responding to future attacks in a proactive mode. Modern hackers are getting smarter at attacking compromised systems using next-generation technologies like deepfakes and generative AI. The blind spots created by this situation require strategic deployment of AI cybersecurity solutions.
The first step in automating enterprise security through an AI-based SOC is understanding which functions need to be automated.
This is an infographic that lists various IT functions that businesses need to automate using AI for cybersecurity.
How is Appinventiv approaching SOC automation through AI in cybersecurity?
At Appinventiv, when we work on enterprise-grade use cases for artificial intelligence in cybersecurity, our primary objective is to automate systems. Once all the security features mentioned above are in automatic detection and notification mode, the next step is to implement AI for cybersecurity with a focus on SOC software.
Here’s a typical implementation plan when working on a project as an AI cybersecurity provider:
- Conduct a needs assessment to identify your organization’s SOC requirements.
- Build software in-house to meet your business’s unique needs for AI in cybersecurity.
- Integrate new AI cybersecurity solutions with your existing security infrastructure.
- Create some detection/response playbooks to use as business standards.
- Test the accuracy and proper functioning of the system.
- Develop processes and policies to use technology and measure the impact of AI on cybersecurity.
- Monitor system performance and make adjustments as necessary.
- Document the AI/ML algorithms used along with implementation steps.
- Create a detailed report on your AI cybersecurity project methodology, results, and recommendations for further improvements.
- Evaluate the effectiveness of your system for monitoring and responding to security incidents.
With extensive experience as an AI development company working with new generation technologies such as artificial intelligence, IoT, and blockchain, we have helped multiple companies, from SaaS companies to manufacturing sectors to fintech companies, implement AI in cybersecurity. We have supported you. Scale to ensure predictive detection and on-time addressing. Contact us to build your AI cybersecurity solution.
Frequently asked questions about artificial intelligence in cybersecurity
Q. How does cybersecurity AI work?
A. Artificial intelligence in cybersecurity analyzes millions of events, user behavior patterns, and threat types to identify potential attacks in real time. Furthermore, it combines the capabilities of IoT, machine learning, and blockchain to create a transparent, real-time ecosystem that notifies stakeholders of malicious events.
Q. How will AI impact cybersecurity?
A. The impact of AI in cybersecurity is to automate repetitive tasks, correlate data, quickly create protection against threats, detect infections in systems, track user behavior and activities, fight bots, Comprehensive risk prediction and landscape analysis. , incident detection and response.
Q. What are some examples of AI in cybersecurity?
A. Examples of artificial intelligence in cybersecurity can be seen through breach, phishing, malware detection, spam filtering, bot identification, thread intelligence, vulnerability management, incident response, fraud detection, network segmentation, and more.
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author
Sudeep Srivastava
Co-founder and Director