While resiliency has always been a focus of EA, “the focus now is on proactive resiliency” to better anticipate future risks, says Barnett. He recommends expanding EA to map not only a business’ technology assets but all its processes that rely on vendors as well as part-time and contract workers who may become unavailable due to pandemics, sanctions, natural disasters, or other disruptions. Businesses are also looking to use EA to anticipate problems and plan for capabilities such as workload balancing or on-demand computing to respond to surges in demand or system outages, Barnett says. That requires enterprise architects to work more closely with risk management and security staff to understand dependencies among the components in the architecture to better understand the likelihood and severity of disruptions and formulate plans to cope with them. EA can help, for example, by describing which cloud providers share the same network connections, or which shippers rely on the same ports to ensure that a “backup” provider won’t suffer the same outage as a primary provider, he says.
To make things a bit more concrete, let’s look at a very simple example that shows the positives of both sides. Developers are the primary audience for InfluxData’s InfluxDB, a time series database. It provides both client libraries and direct access to the database via API to give developers an option that works best for their use case. The client libraries provide best practices out of the box so developers can get started reading and writing data quickly. Things like batching requests, retrying failed requests and handling asynchronous requests are taken care of so the developer doesn’t have to think about them. Using the client libraries makes sense for developers looking to test InfluxDB or to quickly integrate it with their application for storing time series data. On the other hand, developers who need more flexibility and control can choose to interact directly with InfluxDB’s API. Some companies have lengthy processes for adding external dependencies or already have existing internal libraries for handling communication between services, so the client libraries aren’t an option.
“Digital dialogue between trading partners is crucial, not just for those two [direct trading partners], but also for the downstream effects,” he says, adding that when it comes to supply chains and procurement, SAP’s focus is on helping its customers ensure that the data “flows to the right trading partners so that they can make proactive decisions in moving assets, logistics and doing the right purchasing”. He further adds that where supply chain considerations have traditionally been built around “cost, control and compliance”, companies are now looking to incorporate “connectivity, conscience and convenience” alongside those other factors. On the last point regarding convenience, Henrik says this refers to having “information at my fingertips when I need it”, meaning it is important for companies to not only collect data on their operations, but to structure it in a way that drives actionable insights. “Once you have actionable insights from the data, then real change happens, and that’s really what companies are looking for,” he says.
If attackers were able to automate ransomware using AI and machine learning, that would allow them to go after an even wider range of targets, according to Driver. That could include smaller organizations, or even individuals. "It's not worth their effort if it takes them hours and hours to do it manually. But if they can automate it, absolutely," Driver said. Ultimately, “it's terrifying.” The prediction that AI is coming to cybercrime in a big way is not brand new, but it still has yet to manifest, Hyppönen said. Most likely, that's because the ability to compete with deep-pocketed enterprise tech vendors to bring in the necessary talent has always been a constraint in the past. The huge success of the ransomware gangs in 2021, predominantly Russia-affiliated groups, would appear to have changed that, according to Hyppönen. Chainalysis reports it tracked ransomware payments totaling $602 million in 2021, led by Conti's $182 million. The ransomware group that struck the Colonial Pipeline, DarkSide, earned $82 million last year, and three other groups brought in more than $30 million in that single year, according to Chainalysis.
Quantum computing will never exist in a vacuum, and to add value, quantum computing components need to be seamlessly integrated with the rest of the enterprise technology stack. This includes HPC clusters, ETL processes, data warehouses, S3 buckets, security policies, etc. Data will need to be processed by classical computers both before and after it runs through the quantum algorithms. This infrastructure is important: any speedup from quantum computing can easily be offset by mundane problems like disorganized data warehousing and sub-optimal ETL processes. Expecting a quantum algorithm to deliver an advantage with a shoddy classical infrastructure around it is like expecting a flight to save you time when you don’t have a car to take you to and from the airport. These same infrastructure issues often arise in many present-day machine learning (ML) use cases. There may be many off-the-shelf tools available, but any useful ML application will ultimately be unique to the model’s objective and the data used to train it.
All too often, businesses do not see investing in security strategy and technologies as a priority – until an attack occurs. It might be the assumption that only the wealthiest industries or those with highly classified information would require the most up-to-date cybersecurity tactics and technology, but this is simply not the case. All organizations need to adopt a proactive approach to security, rather than having to deal with the aftermath of an incident. By doing so, companies and organizations can significantly mitigate any potential damage. Traditionally, security awareness may have been restricted to specific roles, meaning only a select few people having the training and understanding required to deal with cyber-attacks. Nowadays every role, at every level, in all industries must have some knowledge to secure themselves and their work against breaches. Training should be made available for all employees to increase their awareness, and organizations need to prioritize investment in secure, up-to-date technologies to ensure their protection.
There are two challenges with quantization: How to do it easily - In the past, it has been a time consuming process; and How to maintain accuracy. Both of these challenges are addressed by the Neural Network Compression Framework (NNCF). NNCF is a suite of advanced algorithms for optimizing machine learning and deep learning models for inference in the Intel® Distribution of OpenVINOTM toolkit. NNCF works with models from PyTorch and TensorFlow. One of the main features of NNCF is 8-bit uniform quantization, using recent academic research to create accurate and fast models. The technique we will be covering in this article is called quantization-aware training (QAT). This method simulates the quantization of weights and activations while the model is being trained, so that operations in the model can be treated as 8-bit operations at inference time. Fine tuning is used to restore the accuracy drop from quantization. QAT has better accuracy and reliability than carrying out quantization after the model has been trained. Unlike other optimization tools, NNCF does not require users to change the model manually or learn how the quantization works.
With its distributed and elastic architecture, Apache Druid prefetches data from a shared data layer into an infinite cluster of data servers. Because there’s no need to move data and you’re providing more flexibility to scale, this kind of architecture performs quicker as opposed to a decoupled query engine such as a cloud data warehouse. Additionally, Apache Druid can process more queries per core by leveraging automatic, multilevel indexing that is built into its data format. This includes a global index, data dictionary and bitmap index, which goes beyond a standard OLAP columnar format and provides faster data crunching by maximizing CPU cycles. ... Apache Druid provides a smarter and more economical choice because of its optimized storage and query engine that decreases CPU usage. “Optimized” is the keyword here; you want your infrastructure to serve more queries in the same amount of time rather than having your database read data it doesn’t need to.
At its core, cybersecurity depends on communication. Outdated security policies that are poorly communicated are equally as dangerous as substandard software code and other flawed technical features. Changing human behavior in digital security falls on the technology companies themselves, which need to improve explaining digital security issues to their employees and customers. In turn, tech companies can help employees and customers understand what they can do to make things better and why they need to be active participants in helping to defend themselves, our shared data and digital infrastructure. Instead of competing on the lowest price or claims of best service, how do we incentivize service vendors, cloud providers, device manufacturers and other relevant technology firms to pay more attention to how they communicate with users around security? Rules and regulations? Possibly. Improving how companies communicate and train on security? Absolutely. Shaping a marketplace where tech companies compete more intensively for business on the technical and training elements of security? Definitely.
In the domain of expertise, people base their understanding of transformation on practical insight into the history and culture of the company. A question from an attendee on the panel I conducted illustrated this nicely: “How do you get an organization with a legacy of being extremely risk averse to embrace agility, which can be perceived as a more risky, trial-and-error approach?” The question acknowledges and accepts that the company needs to embrace agility but demonstrates neither insight nor interest as to why it needs to do so. Whether the questioner trusts senior management’s decision to embrace agility, or she has other reasons for ignoring the “why,” it is obvious that she wants to know about the “how.” Too often leaders forget about the how. And that can be a costly mistake. ... “When you have an organization that has been organically growing over 90 years, then the culture is embedded in the language and the behaviors of the people working in the organization,” he said. The strength of legacy companies is that their culture is defined by conversations and behaviors that have been evolving for decades.
Quote for the day:
"The great leaders are like best conductors. They reach beyond the notes to reach the magic in the players." -- Blaine Lee