In this blog post, we will discuss the management and operational principles which underpin enterprise governance in Azure which is a necessity for successful cloud adoption and one of the first rails to enable a culture that facilitates digital innovation.
The core components of Azure management are the challenges of Enterprise Cloud Adoption and the components which make up the full set of governance capabilities in Microsoft Azure.
The Azure governance principles are a continuum of tasks, projects and initiatives, therein you build natively in Cloud and also migrate workloads into Azure, securing and protecting those workloads so that they are robust and resilient. You then proceed to monitoring these workloads, so that you can pick up any problems and ensure that they are consuming resources in a manner which is both performant and cost-effective.
Next, you invest in automated configuration to ensure that any changes to your workloads are holistic but also auditable and immutable. Governance ensures that your workloads and the platform on which they run are compliant with your company’s policies and regulatory obligations. This, in turn, creates a more robust enterprise platform, ready to receive new workloads and in turn, a becomes a hub for innovation with the necessary guard rails in place.
When Westerners think of hyperscale cloud providers, the usual suspects that come to mind are named Amazon Web Services, Microsoft Azure, Google Cloud Platform, Oracle, and IBM Cloud. Seldom do you hear another name, which tends to be odd since it is already the world’s third-biggest cloud service provider according to the numbers: Alibaba Cloud. And with yearly revenue growth between 60 and 140%, they sure are catching up fast.
However, to operate a cloud within China there are some hoops you need to jump and you have to collaborate with the regional administration. Provisioning and relocation times, thus, are fundamentally increased, in no little part since tasks must be directed by the local partners. The truth of the matter is that, while it is conceivable, receiving a cloud foundation that does not have a physical presence in China places organizations that operate in China at a colossal detriment.
Information security aspects when moving operations from on-premise
So if you are reading this I will make some basic assumptions that you know about Microsoft Azure, Amazon Web Services and perhaps even Alibaba Cloud, these are renowned hyperscale cloud vendors. Last few years cloud computing have been among the IT industries hottest topics. The term refers to on-demand access to computing resources provisioned by another provider. 2019 has been dubbed the year of migrations by several vendors and a pronounced advantage of cloud computing is that they tend to be highly available and easily scalable. For fast-growing business, cloud-computing has revolutionized the way they can work. Organizations typically lease cloud-based resources from outside the organization. Of course, it is also possible (but not as common) to host cloud-based services internally.
While cloud computing can be very cost-efficient and offer fast scaling, it’s challenged by the fact that resources will most likely be hosted outside of the business’ data centre and therefore, outside of the direct control of that business, increasing the complexity to manage risk and handle governance.
So in my previous article on quantum computing, we talked about where we are today, and where we are headed in regards to breakthroughs in the technology as well as touching on some basics of “what is quantum computing“. In this article, I explore what quantum cryptography and cryptography is like in a post-quantum world.
So, a refresher: quantum computing is set to transform cryptography due to the revolutionary, non-deterministic way of operating.
How will they affect existing cryptography algorithms and which options do we know today for doing cryptography in a post-quantum world?
For as long as it has been in development inside the science labs of the universities, corporations and government agencies, quantum computing has been considered the next frontier in cybersecurity. Quantum computers are machines that do not work with classical electrical on and off-states but instead rely on quantum states that can be in several states at once, a circumstance known as „superposition(1)“. While they are still in their very infancy, their capabilities have been mystified over and over and it’s probably fair to say that quantum computing is one of the most misunderstood technological advancements of our day and age.
Within the ongoing arms race in the perimeter of information security, artificial intelligence and machine learning are two of the most promising innovations.
While AI in common „personal assistants“, like those developed by Amazon, Alibaba and Google has recently reached levels at which it can convincingly make phone calls on behalf of their users, the capabilities of AI in the hands of defenders, as well as attackers, will likely evolve from buzzword to technology of significant importance over the next years.
On the defensive side, artificial intelligence powered intrusion detection will deliver the ability to pick up on anomalies within an organizations network or perimeters and raise alerts or even countermeasures much quicker than would be possible for any human security team. AI technologies supreme and literally superhumanly quick pattern recognition capabilities enable it to consistently collect intelligence regarding new threats, attempted attacks, acceptable user behaviour and constantly evolve its knowledge. This does allow AI-powered intrusion detection mechanism to find the proverbial needle in the haystack (and react to it) much faster and more concise than classical signature-based intrusion detection systems or a human security analyst.
This does have a flip-side, of course: The same AI capabilities could be used to learn about specific defences and normal user behaviour pattern in an organization and mask the malicious behaviour so it will not be recognized by classical intrusion detection systems or human onlookers.
In today’s interconnected cloud-first, mobile world, securing your online apps and services is vital. However, building secure solutions which deliver value in today’s complex and regulated environment can be a challenge. With information essentially becoming the currency of the digital age, the creation of multiple compliance regulations has forced organizations to implement technical security measures to protect their online systems and customers. Meeting these compliance requirements can be challenging, especially if you are leveraging the benefits of the cloud. Not only do you need to build and configure your apps and services securely, but you also need to ensure your chosen cloud provider meets any necessary compliance requirements.
Compliance in the Cloud Compliance Is a Shared Responsibility
On Azure, Microsoft is responsible for meeting the compliance requirements for its platform while you are responsible for any compliance measures which relate to your cloud service.
With more certifications than any other cloud service provider, Azure meets a broad set of international as well as industry-specific compliance standards. These include the GDPR, ISO 27001, HIPAA, SOC, among others. Microsoft also conducts regular comprehensive audits to ensure it maintains these standards and adheres to the security controls needed.
However, as stated, ensuring your services that are running on Azure meet compliance requirements is your responsibility. Thankfully Microsoft Azure provides a few tools which can help you secure your cloud services and meet the necessary compliance standards.
Over the last few years, cloud computing has been the buzz. Cloud computing services offer an infrastructure that is highly scalable and supports high-performance computing. With high adoption by businesses of all sizes. Development and deployment of applications within the cloud platform are easy and time to market is done in a fraction of the time.
Artificial intelligence is not a new technology. It has been here for a long time and has helped develop computers and software that perform tasks that are associated with intelligence. Machine learning and deep learning are subsets of artificial intelligence that involve the development of algorithms that learn from data inputs and give intelligent output based on that data and the learned patterns.
A lot of research has been done and still is being done on implementing artificial intelligence into cloud computing. Cloud service providers such as Amazon, Google and Microsoft have already integrated AI into their clouds to improve service delivery. AI brings about capabilities such as machine learning, recognition of patterns and robotics to the cloud. On the other hand, the cloud is able to provide a wide range and large volumes of data since these capabilities are largely dependent on data as input so as to produce the desired output. The cloud also allows the systems to open-access and open-source data which is very crucial in facilitating collaborative learning.