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.