Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

The National Institute of Standards and Technology (NIST) Cybersecurity Framework (CSF) has been a stalwart ally for organizations for years, providing guidance on understanding, evaluating and communicating about cybersecurity risks. The release of NIST CSF 2.0, expected in early 2024, provides a paradigm shift. This blog post provides an in-depth exploration of the structure of the NIST CSF and the key changes coming in version 2.0.

The importance of robust cybersecurity practices cannot be overstated, especially when it comes to critical infrastructure, such as the financial industry. Financial institutions, in particular, are entrusted with sensitive data and financial assets, making them prime targets for cyber threats. One crucial aspect of cybersecurity that often goes under the radar is third-party risk management (TPRM).

To understand vulnerability remediation one must first understand remediation in cyber. Remediation refers to the process of addressing and resolving security vulnerabilities or incidents that could potentially pose a threat to an organization’s information systems, data, or network.

Keeper Security’s dedication to protecting user data permeates everything we do. Keeper® holds the longest standing SOC 2 and ISO 27001 certifications in the industry. Keeper is GDPR compliant, CCPA compliant, as well as FedRAMP and StateRAMP Authorized. Our commitment to securing our customers’ data is why Keeper has proactive safeguards in place to protect our customers against automatically filling credentials into untrusted applications or websites.

The advent of cloud computing has revolutionized various industries, with cybersecurity being no exception. In the realm of threat intelligence, cloud computing has emerged as a game-changing force, enhancing the way intelligence is gathered, analyzed, and applied. This post delves into the transformative impact of cloud-based solutions on threat intelligence.

In today’s digital age, cybersecurity is an ever-present concern for businesses and individuals alike. The use of threat intelligence has become a cornerstone in the fight against cyber threats, offering invaluable insights for preventing attacks. However, this comes with its own set of challenges, particularly in terms of maintaining data privacy standards. This guide explores the delicate balance between leveraging threat intelligence for security and upholding user data privacy rights.

The surge of cloud-native applications has propelled Kubernetes into the forefront, revolutionizing how we manage and deploy workloads. However, this exponential growth has also increased the security challenges, and attack surface, DevOps and Security teams must address. As we discussed in a previous blog post, traditional network security measures fall short when presented with Kubernetes’ dynamic nature, demanding a paradigm shift towards more adaptable solutions.

So, you’re interested in cybersecurity! That’s great, because the whole world needs more skilled security professionals. Cybersecurity is the massive practice of “protecting computer and network systems against intrusion, theft or damage. It’s the main line of defense against a vast number of digital adversaries.” The consequences of bad cybersecurity is disastrous, potentially resulting in losses in the millions of dollars.

Despite Python's reputation for simplicity and versatility, ensuring the security of Python programs can be challenging if you or other team members neglect security best practices during development. Additionally, you’ll likely use libraries or other open source projects while building a Python application. However, these resources can introduce additional security issues that leave your program vulnerable to exploits such as command injection.

So you want to use AI-generated code in your software or maybe your developers already are using it. Is it too risky? Large language model technology is progressing at rapid speeds, and policy makers are ill-equipped to catch up quickly. Anything resembling legal clarity may take years to come about. Some organizations are deciding not to use AI at all for code generation, while others are using it cautiously — but everyone has questions.