The number of secrets exposed in public repositories is staggering. With reports in 2021 reaching up to 6 million secrets detected, an increase of 50% from 2020. Secret sprawl is part of every organization, but it is a plague on the open-source world of software development. Even if your organization has a top-notch security-aware culture, human error will inevitably cause secrets to leak and data to be lost or compromised.
While modern businesses depend on data to stay ahead of the competition, data alone isn’t enough. They also need efficient search engines to quickly index and search through millions of records to make sense of the data. Today we’re looking into SOLR and Elasticsearch, the two heavyweights in this domain, to compare their performance differences and use cases.
Cyber threat is soaring on the list of the gravest challenges plaguing organizations today. This is partly an outcome of developers including security in their development process as an afterthought. Although enterprises quickly realize the cyber threat risks to their businesses and reputation, they seem to be in a state of indecision.
You’ll probably agree that there are barely any organizations left that don’t use some form of cloud computing in their daily operations. In fact, the cloud computing market is booming, with various sources expecting a worth of upwards of $600 billion within the next two years. And it makes sense: Cloud computing is the cheaper, scalable, easier-to-manage young cousin of yesteryear’s private server.
If you’re an IT manager or business owner, chances are external security is always top of your mind. But lately, managing access control of documents for employees has become just as necessary to limit access to information and information processing systems. While cybersecurity breaches are a menacing threat, internal security problems can be equally devastating, making access control measures necessary to mitigate the risk of access without authorization.
In the world of Big Data, decentralization is king. Seeing that today’s data sets are so large and complex that traditional database systems cannot handle them, distributed deep learning splits computational problems that require high storage and solving time into many subproblems that can be assigned to multiple machines.
With over 200% YoY Growth and used by 100,000+ Organizations globally, Slack has become an indispensable internal communication tool for teams to coordinate their activities. But when it comes to exchanging information and external sources, certain risks are at play that requires more insight and attention. Slack links to various vital systems within many organizations, making it an easy, central access point hackers can use to reach multiple systems.
Approaching any finish line in life can be exciting yet stressful. This holds especially true in software deployment. The deployment phase is the final phase of the software development life cycle (SDLC) which puts the software into production. After a project team completes their planning, designing, and testing of a software product, the product is ready to go live.
To grasp the concept of a Kubernetes Deployment and Kubernetes Deployment strategy, let’s begin by explaining the two different meanings of the term “deployment” in a Kubernetes environment: Kubernetes Deployment allows you to make declarative updates for pods and ReplicaSets. You can define a desired state and the Deployment Controller will continuously deploy new pod instances to change the current state to the desired state at a controlled rate.
A recent survey of nearly 2,000 IT professionals found that while most (85%) enterprises believe cloud technologies are critical to innovation, only 40% actually have a security policy in place. On top of this, almost half of the respondents using cloud infrastructure reported their engineers and developers circumvent or ignore cloud security and compliance policies, demonstrating the importance of automation and monitoring technology.
