Scope of Artificial intelligence in Cyber Security

By Enterprise Security Magazine | Monday, December 10, 2018

Security is a key component for all organizations around the world and has its various types. Cybersecurity is one of the essential security, organizations invest hefty to secure their data and terminate security breaches. Not just private, government organization and agencies are also striding towards creating a system that is able to continuously learn its environment, identify and predict unknown trends, triages alerts and analyze data to provide actionable context for officials. Artificial Intelligence (AI) is one such technology that holds the capability to make it possible using machine learning as a helping hand.

Traditional cyber security systems are mostly either rule-based or signature-based which require much of human intervention and institutional knowledge. It takes up too much of employee time for a constant update of rules and also bounds the analysts to have a look over a single part of the enterprise, failing to provide them a bird’s eye view of the overall environment. On the other hand, AI augments the human element and makes the time spent more productive. AI analyzes large volumes of data, recognizes complex patterns of malicious activities and provides analysts much accurate approach towards securing and updating their cybersecurity system.

Data, discovery and deployment are three pillars of security lifecycle over which the AI relies. The lifecycle gives insights to agencies about their security ecosystem and helps to understand their stand against modern day threats. Let’s see why each pillar is so important for AI to be successful.

Data: For AI to work it requires data for analyzing. Data can be in either streaming form or stored form as both are valuable for cybersecurity. Large volumes of stored data help in understanding and predicting the trends in the security breach and build a security against them. Whereas, streaming data enables the AI to analyze the real-time threats and terminate them. Private and government both sectors need to share their data to enhance data inventory which will give a more comprehensive understanding of threat landscape.

Discovery: This process is an integral part of the system, with the help of artificial intelligence and machine learning agencies will be able to build new models for supervised and unsupervised purposes. In the cyber domain, there is not much of labeled data which forms a gap between supervised and unsupervised models. A combined approach of both models is capable to bridge the gap between the two as it includes the benefits of both models and reduces their drawbacks to least, leading to new discovering and predicting new threats and build a firewall against them.

Deployment: The power of analytics comes into force now; organizations implement the changes in their system to fights threats and anomalies based on the findings from the discovery phase. Activities such as patching a commonly attacked area or increasing the security of particular system come under this phase of a security system. It is necessary to collect better data, sharing, and adopt advanced technologies like AI.

Undoubtedly, human intervention and supervision will always be required in the system, still, AI has made a robust stand for itself with benefits that it brings with itself. Some key benefits of AI are-

• It is cost-effective.
• Minimizes data breach and improves productivity.
• Supports identification and authentication technologies providing deeper security.
• Saves investigation and detection time.
• Cleans and fixes damaged or infected networks, applications and drives.
• Fast in organizing cybersecurity approaches.

With the increase and advancement in security threats, it has become the need of the hour to incorporate new and advanced technologies to the system and optimize them. AI and machine learning enable to predict such threats and build a system to combat them. 

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