How data science is changing cybersecurity
Cybersecurity is at a critical turning point with many companies conducting more online business than ever before, while hackers are using increasingly sophisticated artificial intelligence (AI) techniques to launch cyberattacks.
That’s why many Data Science companies are using cybersecurity tools that leverage Data Science, AI and Machine Learning (ML) to search through data patterns, figure out where an organization is vulnerable, and eliminate the risk of intrusion.
Cybersecurity powered by Data Science
The central idea behind cybersecurity is, of course, data security. However, cybersecurity built upon a foundation of Data Science is a fairly new approach that allows organizations to detect and prevent threats, often with the help of a Data Science consultancy.
Data scientists can analyze data to determine authenticity, originality and security. Various risks can be predicted by applying data science to ultimately improve an organization’s security. Following extensive data analysis, data scientists can then develop algorithms that block the threats of attack to an organization. These algorithms can also inform AI and ML systems for a more targeted security approach.
Most cyberattacks compromise an organization’s stored data and use that data to conduct fraudulent activities. Fortunately, modern cybersecurity techniques are using Data Science to keep all touch points – from digital devices to IT systems, and software – safe from cyberattacks.
How Data Science consulting supports cybersecurity
1) User and Entity Behavior Analytics (UEBA)
UEBA is a powerful security technology that applies Data Science techniques to identify anomalies in user behavior, which may be caused by an attacker. Suspicious activity can be detected by identifying correlations between abnormal user behavior and security attacks. UEBA techniques can provide an overview of what is really going on in an organization by joining the dots between any abnormalities. The security team can then take preventative measures to protect the organization against further intrusions.
A similar UEBA process can be applied to prevent fraud within an organization. In this case, the chief security officer can detect abnormalities in credit card purchases by using statistical data analysis. This analyzed information can then be used to investigate fraudulent activity and prevent further instances.
2) Intrusion, detection, and prediction
Prior to data science in cybersecurity, security professionals and hackers would play a continual game of cat-and-mouse. Attackers would constantly be improving their intrusion methods and tools, while security officers were constantly looking to improve their detection systems based on information gathered relating to known attacks. The downside was that attackers always had the upper hand in this situation and security teams were applying fixes reactively, rather than proactively.
Cybersecurity techniques conducted by data science specialists use historical as well as current information to predict future attacks. This approach can be backed up with ML algorithms to identify vulnerabilities in the data security environment and improve an organization’s overall security stance.
3) Establishing DevSecOps and DataSecOps cycles
DevOps pipelines are put in place by enterprises to ensure a constant feedback loop and maintain a culture of ongoing collaboration. A DevSecOps cycle includes an additional security element for DevOps teams. This enables DevSecOps professionals to identify the most critical security challenge and then establish a workflow based on that issue.
Data scientists use automation in their workflows, so they know all about DevOps practices. This means DevSecOps can quickly and effectively be applied to data science in a process known as DataSecOps. By using this agile methodology, data scientists can ensure the promotion of ongoing security and privacy across the entire organization.
4) Data protection with Associate Rule Learning
Associate Rule Learning (ARL) is an ML approach used to discover relationships between items in large databases. One of the main types of ARL – market-based analysis – shows relationships between items that people buy most frequently. For instance, a combination of cheese and bread could relate to a sandwich.
ARL techniques can also be used as a tool to recommend data protection measures. ARL studies the characteristics of existing data and delivers automatic alerts whenever unusual characteristics are detected. The ARL system will constantly update itself so that even the slightest deviations in data can be detected.
5) Backup and data recovery
New and innovative data science solutions currently leverage ML techniques to automate repetitive backup and recovery tasks. The ML algorithms in place can be trained to precisely follow the priorities and requirements of an organization’s security strategy.
These backup and recovery systems with ML capabilities provide valuable support for cyberattack response teams in terms of organizing workspaces and resources. For instance, ML tools can identify, access and recommend the right equipment and locations for a particular business recovery plan based on a company’s needs.
As cyberattacks continue to evolve, nobody can 100% accurately predict exactly what form they will take in the future. However, Data Science companies are using innovative technologies such as UEBA and Intrusion Detection Systems that enable businesses like yours to predict possible future threats and gain increased levels of protection against potential malicious attacks.
Get in touch with ProCogia
We are a data science company. Find out how our Data Science consultants can leverage the power of your data with AI and ML techniques to create stronger protection against cyberattacks for your organization.