Certainly COVID-19 continues to globally plague, and the research predicts that connected device makers will double efforts for healthcare. But COVID-19 forced many of those who were ill to stay at home or delay necessary care. This has left chronic conditions unmanaged, cancers undetected, and preventable conditions unnoticed. "The financial implications of this loom large for consumers, health insurers, healthcare providers, and employers." Forrester's report stated. There will be a surge in interactive and proactive engagement such as wearables and sensors, which can detect a patient's health while they are at home. Post-COVID-19 healthcare will be dominated by digital-health experiences and will improve the effectiveness of virtual care. The convenience of at-home monitoring will spur consumers' appreciation and interest in digital health devices as they gain greater insight into their health. Digital health device prices will become more consumer friendly. The Digital Health Center of Excellence, established by the FDA, is foundational for the advancement and acceptance of digital health. A connected health-device strategy devised by healthcare insurers will tap into data to improve understanding of patient health, personalization, and healthcare outcomes.
We’ve been following all the recent reporting and tweets about hospitals being attacked by Ryuk ransomware. But Ryuk isn’t new to us… we’ve been tracking it for years. More important than just looking at Ryuk ransomware itself, though, is looking at the operators behind it and their tactics, techniques, and procedures (TTPs)—especially those used before they encrypt any data. The operators of Ryuk ransomware are known by different names in the community, including “WIZARD SPIDER,” “UNC1878,” and “Team9.” The malware they use has included TrickBot, Anchor, Bazar, Ryuk, and others. Many in the community have shared reporting about these operators and malware families (check out the end of this blog post for links to some excellent reporting from other teams), so we wanted to focus narrowly on what we’ve observed: BazarLoader/BazarBackdoor (which we’re collectively calling Bazar) used for initial access, followed by deployment of Cobalt Strike, and hours or days later, the potential deployment of Ryuk ransomware. We have certainly seen TrickBot lead to Ryuk ransomware in the past. This month, however, we’ve observed Bazar as a common initial access method, leading to our assessment that Bazar is a greater threat at this time for the eventual deployment of Ryuk.
We often think of the term “DevOps” as being synonymous with “CI/CD”. At GitHub we recognize that DevOps includes so much more, from enabling contributors to build and run code (or deploy configurations) to improving developer productivity. In turn, this shortens the time it takes to build and deliver applications, helping teams add value and learn faster. While CI/CD and DevOps aren’t precisely the same, CI/CD is still a core component of DevOps automation. Continuous integration (CI) is a process that implements testing on every change, enabling users to see if their changes break anything in the environment. Continuous delivery (CD) is the practice of building software in a way that allows you to deploy any successful release candidate to production at any time. Continuous deployment (CD) takes continuous delivery a step further. With continuous deployment, every successful change is automatically deployed to production. Since some industries and technologies can’t immediately release new changes to customers (think hardware and manufacturing), adopting continuous deployment depends on your organization and product. Together, continuous integration and continuous delivery (commonly referred to as CI/CD) create a collaborative process for people to work on projects through shared ownership.
Once data is gathered and explored, it is time to perform feature engineering and modeling. While some methods require strong domain knowledge to make sensible decisions feature engineering decisions, others can learn significantly from the data. Models such as logistic regression, random forest, or deep learning techniques are then run to train the algorithms. There are multiple steps involved here and keeping track of experiment versions is essential for governance and reproducibility of previous experiments. Hence, having both the tools and IDE around managing experiments with Jupyter notebook, scripts, and others is essential. Such tools require provisioning of hardware and proper frameworks to allow data scientists to perform their jobs optimally. After the model is trained and performing well, in order to leverage the output of this machine learning initiative, it is essential to deploy the model into a product whether that is on the cloud or directly “on the edge”. ... If you have large set inputs you would like to get the predictions on them without any immediate latency requirements, you can run batch inference in a regular cycle or with a trigger
"New technologies using machine learning, natural language processing, and advanced analytics can help finance leaders fix or work around many data problems without the need for large-scale investment and company-wide upheaval,'' Deloitte said. In fact, such technologies are already being used to help improve corporate-level forecasting, automate reconciliations, streamline reporting, and generate customer and financial insights, according to the firm. Why are CFOs getting involved in data management? "Business decisions based on insights derived from data are now critical to organizational performance and are becoming an essential part of a company's DNA," explained Victor Bocking, managing director, Deloitte Consulting LLP, in a statement. "CFOs and other C-level executives are getting more directly involved, partnering with their CIOs and CDOs [chief data officer] in leading the data initiatives for the parts of the business they are responsible for." As companies generate more and more data each day, finance teams have seemingly limitless opportunities to glean new insights and boost their value to the business. But doing that is easier said than done, the firm noted. The problem is the amount of data emanating daily from various sources can be overwhelming. Deloitte's Finance 2025 series calls this "the data tsunami."
To answer this question, we need to understand how they work, and crucially, what they can’t do. While I’ve spent a great deal of the past year testing these tools and comparing them in like-for-like tests against a human pentester, the big caveat here is that these automation tools are improving at a phenomenal rate, so depending on when you read this, it may already be out of date. First of all, the “delivery” of the pen test is done by either an agent or a VM, which effectively simulates the pentester’s laptop and/or attack proxy plugging into your network. So far, so normal. The pentesting bot will then perform reconnaissance on its environment by performing scans a human would do – so where you often have human pentesters perform a vulnerability scan with their tool of choice or just a ports and services sweep with Nmap or Masscan. Once they’ve established where they sit within the environment, they will filter through what they’ve found, and this is where their similarities to vulnerability scanners end. Vulnerability scanners will simply list a series of vulnerabilities and potential vulnerabilities that have been found with no context as to their exploitability and will simply regurgitate CVE references and CVSS scores.
Ransomware attacks are becoming more rampant now that criminals have learned they are an effective way to make money in a short amount of time. Attackers do not even need any programming skills to launch an attack because they can obtain code that is shared among the many hacker communities. There are even services that will collect the ransom via Bitcoin on behalf of the attackers and just require them to pay a commission. This all makes it more difficult for the authorities to identify an attacker.Many small and medium-size businesses pay ransoms because they do not backup their data and do not have any other options available to recover their data. They sometimes face the decision of either paying the ransom or being forced out of business ... To prevent from becoming a ransomware victim, organizations need to protect their network now and prioritize resources. These attacks will only continue to grow, and no organization wants to be displayed by the media as being forced to pay a ransom. If you are forced to pay, customers can lose trust in your organization’s ability to secure their personal data and the company can see decreases in revenue and profit.
Stack Based Exploits - This is possibly the most common sort of exploit for remotely hijacking the code execution of a process. Stack-based buffer overflow exploits are triggered when the data above the stack space has been filled out. The stack refers to a chunk of the process memory or a data structure that operates LIFO (Last in first out). The attackers can try to force some malicious code on the stack, which may redirect the program’s flow and perform the malicious program that the attacker intends to implement. The attacker does this by overwriting the return pointer so that the flow of control is passed to malicious code. Integer Bug Exploits - Integer bugs occur due to programmers not foreseeing the semantics of C operations, which are often found and exploited by threat actors. The difference between integer bugs and other exploitation types is that they are often exploited indirectly. Likewise, the security costs of integer bugs are profoundly critical. Since integer bugs are triggered indirectly, it enables an attacker to compromise other aspects of the memory, securing control over an application. Even if you resolve malloc errors, buffer overflows, or even format string bugs, many integer vulnerabilities would still be rendered exploitable.
AI and ML play a key role in accelerating digital transformation across use cases – from data gathering and management to analysis and insight generation. Enterprises that have adopted AI and ML effectively are better positioned to enhance productivity and improve the customer experience by swiftly responding to changing business needs. DevOps teams can leverage AI for seamless collaboration, incident management, and release delivery. They can also quickly iterate and personalize application features via hypothesis-driven testing. For instance, Tesla recently enhanced its cars’ performance through over-the-air updates without having to recall a single vehicle. Similarly, periodic performance updates to biomedical devices can help extend their shelf-life and improve patient care significantly. These are just a few examples of how AI-enabled DevOps can foster innovation to drive powerful outcomes across industries. DevOps teams can innovate using the next-gen, cost-effective AI and ML capabilities offered by major cloud providers like AWS, Microsoft Azure, and Google Cloud. They offer access to virtual machines with all required dependencies to help data scientists build and train models on high power GPUs for demand and load forecasting, text/audio/video analysis, fraud prevention, etc.
With a constant focus on innovation in the IoT industry, oftentimes security is overlooked in order to rush a product onto shelves. By the time devices are ready to be purchased, important details like vulnerabilities may not have been disclosed throughout the supply chain, which could expose and exploit sensitive data. To date, many companies have been hesitant to publish these weak spots in their device security in order to keep it under wraps and their competition and hackers at bay. However, now the bill mandates contractors and subcontractors involved in developing and selling IoT products to the government to have a program in place to report the vulnerabilities and subsequent resolutions. This is key to increasing end-user transparency on devices and will better inform the government on risks found in the supply chain, so they can update guidelines in the bill as needed. For the future of securing connected devices, multiple stakeholders throughout the supply chain need to be held accountable for better visibility and security to guarantee adequate protection for end-users.
Quote for the day:
"The great leaders have always stage-managed their effects." -- Charles de Gaulle