The concept of IT based cyber intelligence is gaining momentum rapidly. Almost all major business organizations have reported one or the other kind of cyber attack on their data management systems. In most cases, the companies acknowledged that they have left critical points uncovered and vulnerable to attacks from both internal and external threat agents and vectors, who used technology and stealth skills to inflict damages. Some companies, despite having the best of resources, could not prevent the attacks and worst, are still counting the dollars they have lost to these attacks. In most cases, cyber intelligence teams could have prevented the attacks using AI and advanced data science capabilities that form the new age foundation for security attacks. The core idea of having cyber intelligence teams taking control of security measures is to have more and more data science professionals leading the efforts. This explains the reason why we are witnessing a rapid surge of demand for data science certification in Bangalore.
In this article, we will explain how cyber intelligence teams have evolved and how different technologies from data science domains come together to secure business data in one place.
What is cyber intelligence?
Also called as threat intelligence, cyber intelligence is the advanced form of IT security and risk assessments. The modern cyber intelligence efforts revolve around predictive intelligence used to track, monitor, analyze and provide diagnostic solutions against aggression from external agents. These threats are mostly done to steal data, inflict brute force attacks to damage IT systems and devices connected to a private network, do a terror attack, or do a ransomware attack.
How much does a cyber attack cost to companies?
A low impact data breach could cost a company less than $2000 USD, and a big impact could dent $4000 million into the company’s reserves. At the end of the day, the cost of a cyber attack is measured in monetary and non-monetary terms. In the US alone, where 100s of attacks occur every week, leave a company poorer by $4.5 to $6 million dollars. And, this cost is going up every year! In fact, between 2019 and 2021, the pandemic left companies panting for security cover as they faced attacks from all corners of the world—of which 50% of the losses were reported from new destinations, such as COVID finance providers and insurance claims services. IT companies, SaaS providers, automakers, flight booking systems, and healthcare service providers were some of the biggest losers to cyber attacks in addition to the traditional targets such as banking and currency payment/ international currency exchange platforms. Threat artists have now identified that companies that were once at the pinnacle of cyber security preparedness have left billions unattended and unguarded during the covid, which is now open to snag through phishing, Trojan, and ransomware.
What is the main purpose of cyber intelligence?
The main objective of cyber intelligence is to prepare organizations against different types of threats emerging out of various sources. These sources could be part of zero day attacks or advanced threats or APTs or ransomware attacks. Big organizations spend billions on security measures to salvage business data which is the biggest asset they own at a point where it could be leading in the competition or to stop competitors from gaining advantage from the stolen data, or simply to protect their reputation which is part of the cyber resilience.
In fact, most businesses associate their brand reputation with cyber resilience. For instance, all the major data driven companies to hire specialized cyber experts simply to prevent harm to brand reputation. Companies that are breached occasionally are found to be lacking in risk management and enterprise data management. These companies have a huge gap between their perceived threats and the real loopholes that are either hidden from their IT teams or are only exposed when advanced data science capabilities are used such as AI based predictive intelligence and cyber security management.
Can data science work for cyber intelligence teams?
9 out of 10 cyber attacks could have been prevented by simply scanning through the email services. In fact, business emails are the most targeted accounts which account for $10 million US dollars in corporate theft every year. This is followed by attacks caused by phishing, spam injections, APTs, and social engineering. In less than 200 days, the cost of cyber attacks could expand by 20% if not detected and reported by the chief data officers and chief information officers in the industry. Without data science support, this is impossible to contain and reduce the data breach. We will explain how.
Data science in cyber security and threat intelligence might sound like a relatively new congruence. However, data has been always at the foundation of any digital and forensic activity. As teams became more data savvy and technology arrived to solve many business problems, IT security and threat detection gained prominence. Large volumes of data that were often left unlabelled suddenly became an obnoxious cause of concern for the business owners. These were related to the digital services, systems, and software packages hosted in different locations and networks. In order to keep these safe from attacks, data science teams called upon for a predictive approach — which essentially meant the use of AI and machine learning based intelligence systems that could scan large databases, and secure networks that use, transmit, store and analyze these information sets. From the behavioral analysis of data management teams and IT systems that scanned these to using data science and AI for data protection, organizations managed to improve their security landscape significantly by optimizing the cost of IT security and reducing losses related to spam and malware.
Machine learning next…
Garbage in and garbage out is the next gen approach to cyber security today.
Cyber security is relying on machine learning approaches that are built and customized for use by top level security analysts having data science certification from Bangalore and Gurgaon. By using AI and machine learning in cyber security, professionals are able to identify the various capabilities in cloud automation, data management, security and compliance, blockchain, and robotic automation which could all be potential as a stand alone or in congruence to defeat attackers from entering core or periphery of IT systems.
Python and R programming are considered as the extension of big data analytics that puts machine learning on a fast track execution mode. Using new techniques such as k-Nearest neighbors, SVMs, and clustering, cyber intelligence teams manage to improve their quality of threat detection, even if it means spending more dollars than what it would have caused to go without ML’s help.
Even in an economic recession, it is impossible to imagine the future of a company without cyber intelligence groups that use AI and machine learning. By spending a few thousand extra dollars on data science efforts strategically focused on improving cyber management and resilience, ML based automation can help human managers make a good use when it comes to streamlining IT operations.
Conclusion
As operations continue to move to Cloud and deploy automated IT security systems for security measures, the level of data science excellence would help teams conquer the new world.