A pervasive feature of every business operating in the modern world is working with a lot of sensitive data and various stakeholders. Besides providing the services, it offers, keeping that data safe is an implicit promise to the data owners.
What is Organizational Neural Security?
Organizational Neural Security is a robust security strategy that doesn’t depend on the organization’s structure but the Nature of threats and their origin. By considering the increasing software orientation of the security architecture, this approach is far more ready to handle the newer world’s threats. It is implemented by developing and documenting more general strategies that can be easily adopted in various organizations and branches.
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Although, appropriate implementation of such an arrangement needs organizations to adopt enterprise-grade integration. Every node and branch of the Organization is covered by such a security architecture, thus making it a more holistic approach.
The need for Organizational Neural Security
The security ecosystem that organizations must operate in is constantly evolving, raising newer threats challenging the security architecture in novel ways. Increasing digitalization in various sectors has resulted in a more connected world, making it possible for threats to arise in any part of the world. With this highly volatile security situation, it becomes hard for organizations to keep pace if they only implement environment-specific ad-hoc security architecture. Such a security architecture is often lacking as it has not been tested enough and isn’t backed with any widespread evidence. If any new change is introduced to such a system, it breaks down more often than not. As the organization evolves and develops, such traditional organizational security finds it challenging to integrate the newer branches in its fold. It is also unnecessarily complex and requires vast resources to be invested in its development. It often ends up discouraging organizations from adopting organizational security, making them more vulnerable to potential threats.
The talk around Organizational Neural Security has inspired organizations to look into Deep Learning and Artificial intelligence for solutions pertaining to Cybersecurity threats.
Artificial Intelligence: Artificial Intelligence refers to a machine that has developed the ability to think and act like human beings.
Cybersecurity: It refers to a set of technical and operational solutions to protect machines and networks from threats of unauthorized access by an attacker.
Artificial Intelligence in the Cybersecurity domain
According to a study taken up at the University of Maryland, there is a cyberattack every 39 seconds. Cybersecurity professionals find it hard to keep pace with the numbers and the sophistication and speed of these attacks. It creates the need for a security architecture that is just as fast and efficient. A Machine equipped with decent computational resources and Artificial Intelligence offers a plausible solution to the problem.
Artificial intelligence tries to mimic human intelligence and thus classify all the processes into threats and non-threats. It can identify patterns among data, scrambling for malware and malicious behavior, often seen before an attack. Such a system can either be implemented with Machine Learning or Deep Learning.
Machine Learning is a simpler approach to Artificial intelligence and depends a lot on the developer for marking out features it needs to train and look out for. It makes it more appropriate for linear problems where human beings can visualize the problem, its driving factor, and the possible solution.
On the other hand, deep learning is implemented with the help of layers of neural networks and thus is more suitable for complex and non-linear tasks. These are applied for tasks where the patterns to be detected are beyond human comprehension. Neural networks are a much closer implementation of human Brain intelligence than simple Machine Learning approaches.
A neural network goes through a series of Reinforcement Learning Cycles based on a rewards and punishment approach during the training period. During this training, the Network learns the behavioral patterns of Malwares, Viruses, SQL injections, and other threats that one needs to prevent. Later on, it goes through unsupervised learning for a better and specific threat classification. It provides the AI model with an innate ability to learn by creating an evolving security architecture.
Benefits of AI in Cybersecurity
1. If trained right, it can start functioning at full efficiency on the first day itself.
2. It can self-learn and thus doesn’t depend on human developers to keep it updated. It can evolve with the evolving cyber threats by training on the data available to it.
3. Its operational speed is a thousand times faster than human beings and that with vast volumes of data.
4. The accuracy rate of an AI model can even be higher than 99%. It is difficult even for humans to achieve this level of accuracy.
5. Detection and response to the threats are automated. It frees up Security professionals to tackle other cybersecurity tasks that human beings can perform more efficiently.
The world is changing fast, and so are the threats. But security architectures across various organizations have failed to evolve at a similar pace. As a topic, organizational security has been discussed much but has been rarely implemented in its full force – a result of its limitations. Organizational Neural security seeks to address those limitations with the help of Artificial Intelligence. It is necessary in a more threat-prone world and needs faster and better adoption of a robust security architecture.