Artificial intelligence (AI) techniques are changing the way organizations defend themselves against security threats. In this article, I’ll discuss three primary security technologies — user and event behavioral analytics (UEBA), next-generation antivirus (NGAV), and eXtended Detection and Response (XDR) — and review the machine learning algorithms and techniques they employ.
AI software is based on machine learning (ML) algorithms, which are responsible for sophisticated automated and autonomous capabilities. Machine learning applications in cybersecurity vary, depending on the tasks performed. However, there are certain key machine learning tasks that help make cybersecurity operations more efficient:
Cloud-native technology has made traditional software development models virtually obsolete, eliminating the complexity of monolithic application architectures and streamlining modern development processes. The new paradigm has many advantages, but it brings many new challenges. One of the key challenges is security.
The security concerns of almost all cloud-native applications stem from the nature of cloud-native. When a system’s architecture is relatively stable, security controls can be designed and enforced quite easily. However, when architectures are highly dynamic, like in a cloud-native application, security becomes fluid as well.
The use of containers and serverless features means that cloud applications…
Today’s cloud computing landscape is filled with an incredible variety of services, which provide solutions for organizations of any size. Among these services, there are many that seem similar or even the same, but actually provide different features and capabilities. This article defines and compares the two options: VDI and DaaS.
Virtual desktop infrastructure (VDI) is an architecture that uses a single server to host and serve many virtual machines (VMs), each with a virtual desktop. It is designed to allow organizations to serve controlled desktop environments to users regardless of device or location. …
The field of data science is varied, and today there are many different roles and responsibilities involved in the process. Data science work typically involves working with unstructured data, implementing machine learning (ML) concepts and techniques, generating insights. This process typically ends in a visual presentation of data-driven insights.
Machine learning is a critical element of the process, but training ML models is often a time-consuming process that requires a lot of resources. In the past, gaining access to ML resources was difficult and expensive. Today, many cloud computing vendors offer resources for data science in the cloud.
AWS Step Functions enables you to orchestrate your AWS tasks by using a visual workflow editor. You use this editor to create state machine diagrams, or step functions, which then become the basis of how you build, share, and modify application behavior. You can configure step functions to automatically trigger tasks and processes. This article explains how AWS Step Functions work and examines key pros, cons and best practices.
AWS Step Functions is an AWS service that enables you to orchestrate tasks across AWS services. …
In today’s technological-oriented marketing landscape, businesses find their marketing budgets caving under monthly MarTech (Marketing Technology) fees. That doesn’t have to be the case. You can take advantage of the free tiers and on-demand pricing models offered by AWS to improve your marketing strategy.
A marketing strategy contains the details of your marketing efforts. A solid marketing strategy allows businesses to maintain visibility, generate interest to draw in potential customers, and maintain the interest of returning customers.
Here are key points every marketing strategy should contain:
Most organizations have an efficient workflow to create, release and maintain software. Usually, the highest priority is given to quality assurance testing over security testing. However, the increasing concerns and business risks associated with insecure software have brought increased attention to the need to integrate security into the development process.
A secure software development lifecycle (secure SDLC) involves embedding security assurance into all stages of software development.
A software development lifecycle (SDLC) is a framework that defines the different stages that a software product goes through from beginning to end of its life.
A typical SDLC involves the following stages:
Cloud-based storage is growing, allowing companies to scale their storage capacity regardless of their computing power. Backing up the data stored in the cloud is just as important as backing up your own computer. Amazon Web Services (AWS) offers a solution in the form of Elastic Block Store (EBS), which you can use to back up your -servers (EC2 instances).
EBS is a block storage solution, which organizes the stored data into bytes and arranges them into blocks, each of acting as a separate hard drive. In the world of AWS, blocks are referred to as EBS volumes.
TensorFlow is a highly capable deep learning framework that allows you to train various Deep Learning (DL) models for all kinds of computer tasks such as object recognition, detection and tracking, image classification and speech detection. This introduction will explain the basics of TensorFlow and why it is a great solution for the development of deep learning models.
Developed by Google, Tensorflow is an open-source AI library for machine and deep learning designed to implement, train, test and deploy Artificial Neural Networks (ANNs). …
The open source Relational Databases (RDMS) market is exploding with huge adoption of mature solutions, and new, innovative tools making their debut. One of them, CockroachDB, is gaining popularity with its improved functionality and automatic scalability.
In this article, we’ll introduce you to CockroachDB, compare CockroachDB vs PostgreSQL on features and specific use cases, and help you understand if you should stick with good old Postgres or make the switch to the strangely named contender.
An open source, cloud-based transactional datastore designed to store copies of data in multiple locations to increase the speed of access. …