AI and Automation – A Combo to Manage Cyber Security Threats
Artificial Intelligence and Automation are being used in multiple industries to make the lives of people better and to make things more efficient for organizations. AI is being used to diagnose medical conditions or offer legal advice. Automation is being used in factories to reduce reliance on manual labor and to improve the quality of products. As AI and Automation are becoming more universally applicable concepts and can be implemented irrespective of the industry. These innovative technologies depend on an organization’s critical data that today is threatened by various factors. These factors will attempt to breach the data for financial, competitive or political gain. AI and Automation combination can be leveraged to counter these threats and provide insight into obfuscated malicious activity on systems and networks.
Cyber Threat statistics 2017
Gemalto, a digital security firm based out of Europe, published the Gemalto’s Breach Level Index recently. This shows there were 918 data breaches, resulting in 1.9 billion data records being exposed — a “staggering” 164% increase over the last six months of 2016. Of all breaches, 74% came from malicious outsiders — an increase of 23%. Malicious insider attacks, while they comprised only 8% of all breaches, led to 20 million records being compromised, compared with 500,000 in the last half of 2016 — a 4,114% increase.
With the growing capabilities in the field of AI/Machine Learning, cybersecurity experts are expecting that AI and Automation would help deter and manage cybersecurity threats. This article reviews how AI and Automation can help in the never-ending battle of cybersecurity.
AI + Automation = (Cyber Security)n
Security Automation as a Solution:
Automation involves a range of competencies such as Process Automation, Test Automation, Security Automation. Security Automation is the point of contention now. Security Automation is designed to reduce risks, operational errors and to address the Cyber Security Threat issues often come from abnormal use of data. According to a survey, 91% of the companies tried to save the time and effort required for the analysis which affected their incident response effectiveness.
Security tasks are often prone to error when it comes to processing large volumes of data and making Quick, Reliable and near to Accurate solutions. The Q and R can be achieved through Security Automation whereas the A solution is elaborated in the AI as a Solution section.
Problems faced by Enterprises without Security Automation:
- The amount of ransomware injections into a system by cyber criminals is exponentially higher than the threat detection intelligence measures being taken by the enterprises.
- Cybersecurity teams are inundated with alerts 24*7 but face issues with staff shortage.
- Delayed investigations can lead to devastating data breaches if there is sub-optimal response for threat detection.
Security automation helps security analysts to be more proactive and innovative. It also helps in to focus on complex types of attacks and be prepared for them.
Security Automation Implementation:
Though the concept of Dev-Ops is extending the frame of Security automation, Security Automation can be accomplished across tools and processes as follows:
- Deployment automation
- Infrastructure automation
- Security monitoring tools automation
- Automated threat detection to combat Intruders
- Threat response automation
- Security workflow automation
A challenge on the journey to security automation is the quality of intelligence. Cyber threat intelligence is often prone to false positives due to the unpredictable nature of Internet of Things. Threats can change instantly from one second to the next. Security Automation is a delicate process which needs to be implemented and executed with care, failing which can lead to more serious consequences.
AI as a Solution:
Though the hypothetical advanced Security Automation is very relevant, there arises a question of credibility because of the complexity in real-time execution. So behavioral analytics and machine learning are the advanced forms of automation for Security Automation. They also leverage the alert, monitoring, and prioritization tasks to the next levels. This technology can learn from gradual training and failures which can easily and immediately catches any abnormal behavior. This statistically scores the priority of each potential threat that should be investigated.
AI and machine learning are becoming significant allies in cybersecurity. Machine learning will be strengthened by data-heavy IOT devices and predictive applications to help safeguard the network. AI and machine learning are valuable tools to combat the threat landscape. However, there are still a few hurdles when it comes to implementing the technology.
Improving the quality of threat intelligence is the next step to enabling IT teams to pass more control to AI. The security industry cannot pass complete control to machine automation. There needs to be a balance between operational control and critical exercise that can escalate up to humans. This will ensure that AI and machine learning applications for cybersecurity defense are truly effective.
Why AI is a solution
- Better Decision Making
- Quick Resolution
- Consistent and Stable Root Cause Analysis
- Predictive analysis, diagnosis, and recommendations
The goal for AI in cybersecurity is for it to constantly adapt to the expanding attack surface. Currently humans are connecting the dots, distributing data and applying it to systems. In the future, a mature AI system could make these complex decisions which presently require intelligent correlation through human intelligence.
The combination of Automation and AI solutions are emerging as the ideal combination for streamlining and strengthening a line of defense of Cyber Security that will stand the test of time. Secure@Source from Informatica applies machine learning concepts on the information that is has about sensitive data and users who access that data to provide alerts on abnormal user activity. Since these alerts are specific to sensitive data, it reduces false positives by a large number, improving the efficiency of the Security Operations.