Blockchain technology, or distributed ledger technology (DLT), as it is alternatively often called, is one of the hottest topics in the technology sector as of now. A blockchain is a specific type of distributed ledger that stores data in blocks that are linked together via a cryptographic signature function.
This, in short, works by always using the signature of the last block plus the data of the current block to sign the current block. Given enough computing power behind creating the hash signatures for new blocks, a process that is known as mining (PoW), the resulting public ledger is virtually unmodifiable for malicious actors, commending itself for applications that rely on mutual trust where trust cannot be easily applied.
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.
It leverages a combination of trusted execution environments, advanced cryptography and innovative blockchain-focused consensus mechanisms to enable new ways of utilizing the blockchain. Coco stands for Confidential Consortium.
If you want a deeper dive, I suggest you check out the Coco Framework whitepaper, here.
Additionally, Microsoft offers BaaS (Blockchain-as-a-service) and was chosen by Bankchain which is a platform for banks that want to implement blockchain technology; members include State Bank of India, ICICI Bank, DCB Bank, Kotak Mahindra Bank, Federal Bank, Deutsche Bank and UAE Exchange.
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.
What does that mean? Just that I now am certified to attest that I have a baseline knowledge when it comes to how to go about penetrating a network or a computer system but with the purpose of finding and fixing security vulnerabilities within an ethical framework.
Now you might ask, why should I use a security baseline? First off – it’s for OS hardening, and it saves you a lot of manual work by having ready made settings setup and gives you the importable GPOs, as well as a multitude of custom ADMX files with them visually laid out for you in a spreadsheet.
This allows you to tweak your settings to what best suits your environment.
It’s an incredibly helpful tool for image building, particularly for those of us in verticals that require constant vigilance.