What are some development choices about the application layer that affect the security responsibility? If the low-code application is strictly made up of low-code platform native capabilities or services, you only have to worry about the basics. That includes application design and business logic flaws, securing your data in transit and at rest, security misconfigurations, authentication, authorizing and adhering to the principle of least-privilege, providing security training for your citizen developers, and maintaining a secure deployment environment. These are the same elements any developer — low-code or traditional — would need to think about in order to secure the application. Everything else is handled by the low-code platform itself. That is as basic as it gets. But what if you are making use of additional widgets, components, or connectors provided by the low-code platform? Those components — and the code used to build them — are definitely out of your jurisdiction of responsibility. You may need to consider how they are configured or used in your application, though.
ClusterFuzzLite enables you to run continuous fuzzing on your Continuous integration and delivery (CI/CD) pipeline. The result? You’ll find vulnerabilities more easily and faster than ever before. This is vital. A 2020 GitLab DevSecOps survey found that, while 81% of developers believed fuzz testing is important, only 36% were actually using fuzzing. Why? Because it was too much trouble to set fuzzing up and integrate it with their CI/CD systems. At the same time, though, as Shuah Khan, kernel maintainer and the Linux Foundation’s third Linux Fellow, has pointed out “It is easier to detect and fix problems during the development process,” than it is to wait for manual testing or quality assurance later in the game. By feeding unexpected or random data into a program, fuzzing catches bugs that would otherwise slip past the most careful eyeballs. NIST’s guidelines for software verification specify fuzzing as a minimum standard requirement for code verification. After all as Dan Lorenc, founder and CEO of Chainguard and former Google open source security team software engineer, recently told The New Stack,
When it comes to a foundational sound money, Bitcoin is unmatched. Compared to other blockchain assets, Bitcoin has had an immaculate conception.Also, Bitcoin has an elegantly simple monetary policy and an immutable supply freed from human discretion – something no other cryptocurrency asset can provide. Bitcoin's monetary policy is based on algorithmically-determined parameters and is thus perfectly predictable, rule-based and neither event- nor emotion-driven. By depoliticizing monetary policy and entrusting money creation to the market according to rule-based parameters, Bitcoin’s monetary asset behaves as neutrally as possible. Bitcoin is truly sound money since it provides the highest degree of stability, reliability and security. Most crypto enthusiasts would probably object that while Bitcoin might be the soundest money, its technical capabilities do not allow for DeFi to be built on top of it. As a matter of fact though, nothing could be further from the truth.
The first step businesses need to take to increase the security of their customer data is to review what types of data they're collecting and why. Most companies that undertake this exercise end up surprised by what they find. That's because, over time, the volume and variety of customer information that gets collected to expand well beyond a business's original intent. For example, it's fairly standard to collect things like a customer's name and email address. And if that's all a business has on file, they won't be an attractive target to an attacker. But if the business has a cloud call center or any type of high touch sales cycle or customer support it probably collects home addresses, financial data, and demographic information, they've then assembled a collection that's perfect for enabling identity theft if the data got out into the wild. So, when evaluating each collected data point to determine its value, businesses should ask themselves: what critical business function does this data facilitate. If the answer is none, they should purge the data and stop collecting it.
Evidently AI works by analyzing the training and production datasets. It maps the data from features in the training data to their counterparts in the production data. ... Thereafter it runs different statistical tests depending on the input. Evidently AI then creates graphs that are based on the plotly python library, and you can read more about the code in their open-source GitHub repository. For binary categorical features, it performs a simple Z-test for a difference in proportions to verify if there is a statistically significant difference in how often the training and production data have one of the two values for the binary variable. For multivariate categorical features, it performs a chi-squared test, which aims to see if the distribution of the variable in the production data is likely based on the distribution in the training data. Finally, for numeric features, it performs a two-sample Kolmogorov-Smirnov test for goodness of fit that assesses the distributions of the feature in the training and production data to see if they are likely to be the same distribution, or if they vary from each other significantly.
The Eagle is a quantum processor that is around the size of a quarter. Unlike regular computer chips, which encode information as 0 or 1 bits, quantum computers can represent information in something called qubits, which can have a value of 0, 1, or both at the same time due to a unique property called superposition. By holding over 100 qubits in a single chip, IBM says that Eagle could increase the “memory space required to execute algorithms,” which would in theory help quantum computers take on more complex problems. “People have been excited about the prospects of quantum computers for many decades because we have understood that there are algorithms or procedures you can run on these machines that you can’t run on conventional or classical computers,” says David Gosset, an associate professor at the University of Waterloo’s Institute for Quantum Computing who works on research with IBM, “which can accelerate the solution of certain, specific problems.”
Industrial applications, however, present some unique challenges for computer vision systems. Many organizations can’t use pretrained machine learning models that have been tuned to publicly available data. They need models that are trained on their specific data. Sometimes, those organizations don’t have enough data to train their ML models from scratch, so they need to go through some more complicated processes, such as pretraining the model on a general dataset and then finetuning it on their own labeled images. The challenges of industrial computer vision are not limited to data. Sometimes, sensitivities such as safety or transparency impose special requirements on the type of algorithm and accuracy metrics used in industrial computer vision systems. And the team running the model needs an entire MLOps stack to monitor model performance, iterate across models, maintain different versions of the models, and manage a pipeline for gathering new data and retraining the models.
Quote for the day:
"A leadership disposition guides you to take the path of most resistance and turn it into the path of least resistance." -- Dov Seidman