5 Uses of Automation in Cybersecurity
If you closely look at the following cybersecurity challenges, the traditional methods are not powerful enough to combat the latest cyber-attacks.
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Cybersecurity has turned out to be the greatest challenge for businesses in recent years. Just a year ago, data breaches exposed 4.1 billion records.
As per the Cost of Data Breach Report by Ponemon Institute and IBM Security, a data breach's global average cost touched $3.92 million in the same year.
Considering these reports, it is not difficult to understand why the global spending on cybersecurity is expected to reach $133.7 billion by 2022.
However, if you closely look at the following cybersecurity challenges, the traditional methods are not powerful enough to combat the latest cyber-attacks.
- The number of cyber-attacks is exponentially higher than the threat detection intelligence can identify.
- Responding manually to the massive number of alerts every day is not possible.
- Investigations are too long, which results in massive data breaches.
- The current protective measures cannot match the pace that attacks spread.
This holds because, as per IBM’s report, the average time taken to identify and contain a breach is 280 days, which cost $3.86 million on an average.
This is exactly where automation in cybersecurity is a useful rescuer.
Cybersecurity automation eliminates the need for manual performance of repetitive tasks. Hence, it can provide better results at a faster rate and perform more efficiently.
This is why 88% of cybersecurity professionals believe that automation will make their jobs easier.
Let us see how automation is enhancing the power of cybersecurity.
1 – Automated Threat Correlation
Considering the ever-increasing volume of cyber threats, companies are continually enhancing their network defense. Continuous security testing helps to spot the new variants of malware, trojans, and ransomware.
Automated threat correlation is playing a significant role by activating proactive protection. This solution provides defence not only against the known threats but against the unknown threats as well.
Automated threat correlation works in three stages:
- Collection: In this step, all the available threat intelligence and data feeds are gathered in different ways and uploaded to a central repository database system. In some cases, an automated process pulls sensor log files from a corporate network, while in other cases, the process collects data from individual devices.
- Consolidation: This process goes through normalization or aggregation. All the irrelevant data and many of the false positives are filtering out automatically using machine learning. The security solutions and their users define important data.
It also weeds out duplicate data and ensures that each data is in a standard format to prepare it for the next step.
Correlation: The final step involves data pulling from multiple security platforms, analysis of the data, and conducting correlation of data. The automated process runs appropriate queries to get responses and get accurate intelligence for the threat response team.
Machine learning and automation together make a massive number of data sequencing faster, more effective, and highly accurate.
2 — Automated Penetration Testing
Penetration testing is an in-depth assessment of an organization’s cybersecurity done manually by an individual operator or a team. This testing can access the target network and identify internal issues in the network.
Hence, it can test potential attack vectors with wider possibilities, such as social engineering and phishing attacks. The entire process involves a lot of repetitive tasks that increase process and analysis time.
Automated pen testing automates the repetitive actions of pen testers. This way, they can perform more in less time.
The penetration tools are designed to provide a high degree of customization to help skilled pen testers adapt their efforts to the scope and the testing goals.
The automated pen-testing tools enable the tester to identify the gaps in the security controls. It can analyze and define which techniques were used to escape other controls.
3 – Automated Vulnerability Scanning
Vulnerability scanning is an automated search process where deep scanning is performed to discover vulnerabilities. Vulnerability scanning should not be confused with penetration testing.
In this process, several different vulnerability scanners are used. They search for signatures of known vulnerabilities or regular security errors like using weak passwords. The scanning is done in two ways:
External vs. Internal Scan: An external scan is performed to detect vulnerabilities that could be exploited by external attackers. The internal scans are done for testing insider threat scenarios.
Authenticated vs. Unauthenticated Scan: An authenticated scan discovers the vulnerabilities that might allow an attacker to access through a user account. An unauthenticated scan is for finding the attackers who have not reached this access level.
The combination of all four possible scans will be a better idea to detect all potential vulnerabilities.
Vulnerability scanning is a continuous process to discover new vulnerabilities that might appear every day. The automated vulnerability scanning process can work continuously, notify a security team about the vulnerability and take action as quickly as possible.
4 – Automated Patch Management
The system software is updated automatically regularly. This can cause vulnerabilities that hackers could potentially breach. Software companies release patches, meaning codes created to fix any vulnerability. However, patches may not necessarily make it to the operating system, leading to the scope of a cybersecurity breach.
Automated patch management locates, tests, and applies necessary code alteration to keep the system secure. The process works independently, irrespective of the fact that whether the software manufacturer’s patch is installed or not.
However, patch management is extremely critical to be a well-defined process, and therefore, at some point, human intervention may be needed. Yet prioritizing patches, initiating, deploying, and reporting is efficient.
5 — Automated Traffic Log Analysis
It is crucial to study HTTP/HTTPS traffic logs collated for detecting malware activities over an extended period. The logs have to be processed at different levels like the user, unit, company, industry, and regional for better malware detection.
Indeed, the process is intricate, lengthy, and needs extreme impeccability, which is not possible manually. This is where automated traffic log analysis comes into play that uses a machine-learning algorithm.
Machine learning algorithms are programmed to process traffic logs to identify suspicious traffic, segregated into a channel. The features of the channel are then analyzed based on the profiles of the user, company, industry, and region.
When the malware is detected, the user is immediately updated by sending out a signal to the firewalls and proxies to block the threats. These signals are sent through the Protection API and the dashboard.
The machine learning algorithm analyzes the current traffic logs and historical traffic log files to detect the initial point of infection.
Besides, the identified malware is also downloaded to a sandbox environment for reviewing at the advanced-level. At this level, if the malware is found to be of the botnet category, the data is transferred to the Botnet Interception Module. The module tracks and analyses the traffic and detects compromised users and IP addresses.
The automation in cybersecurity helps to predict potential risks and retaliate to them effectively. While it also minimizes human involvement in handling security activities, especially where the process is repetitive.
The stats shared clearly suggest that data security is at its all-time high risk. Embracing automated cybersecurity is an assured solution to prevent cyber-attacks and data breaches. Indeed automated cybersecurity in the future.
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