right security personnel: Infra and network administrators have the most vital role to play in hybrid cloud computing. Employment of correct personnel with adequate knowledge and experience can apprehend potential threats beforehand and therefore can accommodate the system accordingly.
Endpoint security: Hybrid cloud has more endpoints than on-premise system. Each open endpoint is also entrance for potential attackers. Strict security measures should be application for all inbound data.
Multi-faced approach: It basically deals with both internal and external vulnerabilities. Protecting our network from external threats is not enough here, we need to concentrate on the internal threats also as the frequency of internal threats is higher than external and also it has bigger effect onto the organization.
Pervasive encryption: It is a consumable approach for in flight and in rest data encryption. Huge amount of data can be encrypted easily and in cost-effective way. IBM Z15 is a platform which provides pervasive encryption in digital enterprise [26].
1.7 Use of AI in Hybrid Cloud
Organizations now completes for the customer satisfactions and operational efficiency. AI and machine learning are the light bearer in this regard [12, 13]. A term in this regard often circulated, i.e., “AI in the cloud”. Most of the cloud-based services provider designs their management tool where some AI-based technologies are incorporated. This helps the customer a range of facilities starting from image recognition to big data analysis [24]. The biggest advantage of this adaptation is that you do not need expertise for deployment, or configuration or management of the architecture models and prototypes are already there for the developers. Banking sectors, automobile industries, e-commerce trades, etc., all the approaching toward hybrid cloud and AI. For example in case of banking sector, they can keep their customer details in private datacenters and can leverage public cloud services for operational need. AI will help to analyze customer specific data, which can help the organization to provide loans or offers customized according to the present need of the customer. Also with help of the AI, 24x7 service can be provided, without actual intervention of customer care executives. AI can also help in incident analysis. If sometimes the system became irresponsive, AI can judge the situation and can suggest recovery measures. Disaster recovery plans can also be toughen with the help of AI. Based on the previous observations, AI can generate the pattern of the system might causes disaster, and therefore, preventive actions can be taken in advance. Automobile industries also approaching toward adaptation of AI and hybrid cloud to provide best ever services and hazard free maintenance. In case of e-commerce business, AI has most lucrative role to play. Analyzing the buying pattern of the customer not only helps business to expand, it also can serve the customer need in best possible way.
But is this adaptation sustainable is a million dollar question. Some experts commented that use of AI is a fashion. But the easy-to-use nature and strong data mining methods makes is worthy. Now, what about data privacy? Is the use of AI has any role to play in data privacy? The answer is yes. In this case, AI has a major role to play. It can analyze the attacker’s behavior and attacking pattern and accordingly can guide the network admin to take corrective steps and measures.
Mainly, AI requires large amount of data for their data mining operations and these data may come from several sources some internal and some external. When AI is used in public cloud, we might hesitate to use the data which is highly secure. But the quest for data of an AI engine is huge, as a result potential threats may arise. In July 2019, an incident was happen between AWS and financial giant Capital One. A person was arrested for hacking the data from the server of Capital One containing customer financial information. That person was a former employee of AWS. Capital One uses AWS for sorting the data and also on top of cloud; they built their app for analyzing the data. FBI then called and they investigated that there was some issue in the firewall of Capital One, buy using which the intruder has gain access to the data. AWS quickly responded that there was no issue from their end and Capital One rectifies the misconfiguration in the firewall. But the data breach has already happened. So, cloud security still in immature state as old approaches for securing internal data does not go the cloud. Here comes the hybrid cloud. Data security can be ensured efficiently and adaptation of AI-based technologies is also possible as internal data are kept hidden in private data centers, public cloud has no access to it.
Nutanix [27] provides a solution which is a turnkey for infra, aps, ops, and disaster recovery. A ready-made platform which helps to make a secure private cloud, streamline manual boring data operations, provides less complex management of database by using a single policy for all data, manages data in a better way by storing all data in a single storage, secures all data by providing visualization of policy and traffic works in different segments, detects and quarantines infected portion of the network, automates IT operations with the help of AI and ML, and does periodic backup of all data. This type of solution is highly acceptable in the industry. Special use of AI makes Nutanix very attractive for building secure hybrid cloud.
The amalgamation of AI into cloud has bring an evolution as AI was still complex, expensive, and high-end technology which was out of the reach of the general masses [25]. But now services of AI can be utilized by general masses without actually knowing the background technology. Several mobile apps and IoT work in this regard in an effective way. Human like interfaces, self-service, and customer-oriented application are made possible through the use of hybrid cloud and AI.
Use of AI made hybrid cloud more intelligence by playing a key part in cost analysis, real-time decision-making, policy optimization, and workload distribution leaving the IT experts to work on complex things rather than doing trivial tasks. AI-as-a-service is heading toward next level. Cloud giants AWS, Microsoft, IBM, and Google all are doing investments on this to make their service better as this is a competitive world. Whoever will provide better service in less cost will be more popular.
Quantifi [28] is one such solution. They are using AI/ML to perform risk analysis, data analytics, portfolio management, and trading predictions. Some major banking sectors, asset management companies, pension funds, and financial institutions are their customer. By the use of AI, big data, Lamda architecture, and in-memory computing, Quantifi is one of the front benchers in risk assessment. Cross platform which supports Windows, Linux, MacOS, and Adaptation of ELT layer provides various external data sources to communicate bi-directionally at ease; rich API helps to provide tools and functionalities to clients so that they can extend their business without knowing much of the technical details; real-time business analysis and predictions are just one click away. Quantifi uses Microsoft Azure for its cross platform application development.
IBM Watson [29, 30] is another pioneer in this regard. It a tool where can give their instructions using natural language. This AI-powered search engine can answer complex business queries on demand. The natural language understanding capability makes it more interesting as otherwise for getting high-end business insight may be lines of code has to be written. Watson is also used with other AI-based tools for providing a platform which provides better customer services. This allows client to run all Watson products, IBM’s own AI products on IBM cloud or any cloud from other vendors. It can club private cloud services also.
Lots of other tools are also available which use AI and hybrid cloud for better customer services.
1.8 Future Research Direction
The adaptation of hybrid cloud still is in nurture state. It is not exhaust in nature. High possibility of amalgamation of different technologies makes it useful and difficult to handle also in some cases. Here is a list of future research directions:
Standardization is a vital part of cloud architecture. No uniform standard is there for setting up the hybrid cloud architecture. Research can be done
