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Are you adopting a Zero Trust approach in your organization? 🌐

Traditional cyber security measures relied on fortifying an organization’s firewall. Everything inside the organization was secure, everything outside was not.

Yet, in an ever-evolving tech sphere where all interactions are becoming digitized, more sensitive data needs to be protected.

“Never trust, always verify.” is what the Zero Trust approach is all about.

This cybersecurity approach assumes every digital interaction is a possible breach; therefore it requires users' access to be constantly authenticated, authorized, and validated.

As the threats to tech solutions’ security are increasing, especially when the boundaries of trust are stretched by practices like remote work, Zero Trust is the answer.

In a remote work context, perimeter-based security models would be ineffective as they rely on users being within an organization’s secure network perimeter. Of course, that’s not the case.

By implementing the Zero Trust model, an organization can extend security to any network or device its employees use to gain access to protected data by limiting access to the necessary data only and constantly verifying user identities.

This approach also applies to third-party service providers interacting with the organization’s secure data. SaaS and PaaS solutions fall under the same umbrella. Assuming these interactions are secure can compromise the organization’s cybersecurity and cost them greatly.

Do you plan on adopting Zero Trust for your organization?
Let us know in the comments ⬇

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You might have heard the term “Synthetic Data” being used lately in reference to emerging AI tools 💻

But how much do we really need synthetic data?

Synthetic data refers to computer-generated data that mimics the behavior of real data. These information datasets are manufactured for training and testing artificial intelligence and building efficient machine learning models.

Modern day software engineers rely heavily on synthetic datasets.

They are less costly than collecting authentic real-world data, they don’t infringe on privacy or copyright laws and regulations that protect sensitive user data, they reduce bias by creating diverse datasets that contain the rarest occurrences, and also provide maximum control over the generated data.

In the world of Artificial Intelligence (AI) and Machine Learning (ML), scientists are using synthetic data to fuel numerous innovations.

One widespread application of synthetic data can be seen in the field of computer vision. It allows engineers to train and test self-driving vehicles in realistic environments without having to drive in the real world.

Autonomous vehicles require intensive training on edge cases to prepare for the unpredictability of real-world driving incidents. These edge cases are easily supplied to AI models using synthetic data.

In your opinion, how will synthetic data applications evolve in the upcoming years?
Let us know in the comments ⬇

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Would you like to be interviewed by a robot? 🤖

30 years ago that would have sounded like a line straight out of sci-fi fiction. Today it’s a reality.

AI and machine learning are becoming increasingly involved in the recruitment process in small and large-scale businesses.

Almost 90% of companies around the world are using technology to support HR functions or perform them completely.

AI can screen candidates and evaluate their skills, digitize interviews and analyze them, while chatbots communicate with job candidates throughout the recruitment journey.

In fact, many reports concluded that employers are expecting their HR personnel to be competent in using AI tools to recruit as well as maintain the workforce.

Examples of ongoing HR functions supported by AI and machine learning include scanning employee performance to detect areas that require further training and development, automating PTO or annual appraisals, and even detecting changes in employee engagement.

Despite its numerous benefits, there are concerns that involving machine learning in recruitment processes or human resource management can lead to solidifying flawed systems plagued with bias.

This means that in environments where AI models are trained using discriminatory data, the ideal candidates may be selected based on majority traits and profiles rather than skills and professional merits.

On the other hand, AI advocates argue that machine learning can actually eliminate bias and produce a more objective recruitment experience. By training the AI using unbiased data instead of a company’s history, the AI can break the cycle and make the recruitment process more inclusive.

AI’s impact on all business aspects continues to grow and reshape job performance. There’s no telling how it will advance beyond its present limitations.

Does your company use AI in recruitment or human resource management?
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What is FastAPI? 💻

Welcome to this new series where we talk about the latest in the tech world and what you should keep an eye on.

💠 Today, we’re talking about FastAPI…

FastAPI is a modern, high-performance, web framework for building APIs with Python 3.7+ based on standard Python type hints.

The reason why FastAPI has stood out recently is that its high performance makes it among the fastest Python frameworks and on par with NodeJS and Go.

It significantly increases the speed of feature development and reduces the occurrence of bugs and developer errors.

FastAPI offers exceptional editor support, and minimizes debugging time and code duplication as it allows for multiple features from the same parameter declaration.

Many developers reported that the framework was easy to learn and use which didn’t consume their time in reading the documentation.

The framework produces production-ready code that is fully compatible with the open standards for APIs.

Major corporations are adopting the framework into certain aspects of their software:

Netflix built its crisis management orchestration framework, Dispatch, using FastAPI.

Uber also adopted the FastAPI library to create a REST server that can be queried to obtain predictions for Uber’s code-free deep learning toolbox, Ludwig.

Have you heard of FastAPI?
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Meet Rania El-Baz, the Front End Software Engineer! 👩‍💻

Rania is a passionate frontend developer with an eye for details.

She is a dependable team player and a fast learner.

In her spare time, Rania loves learning about new software development technologies and enjoys gaming.

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