The bigger issue is most of the sectors using digital technologies or integrating emerging technologies do not have a digital risk element defined by the sectoral regulators till date. A lack of National cyber strategy highlighting the key risk to these sectors is still awaiting cabinet nod. Hence, fighting ransomware, advanced persistent threats and malware is becoming tough for the industry, which doesn’t have a framework to rely upon to test or audit their systems. Earlier this year, the European body, ETSI, released consumer IoT security standard. The standard specifies high-level security and data protection provisions for consumer IoT devices which includes IoT gateways, base stations and hubs, smart cameras, TV, smart washing machines, wearables, health trackers, home automation systems, connected gateways, refrigerators, door lock and window sensors. This standard provides a minimum baseline for securing devices and sets provisions for consumer IoT. It lays the foundation for setting strong password controls for IoT devices by stating all consumer IoT device passwords must be unique. In India, and across the world, we see consumer IoT devices getting sold with universal default usernames and passwords.
Before jumping into the coding section, first, we need to understand some design concepts. Since we are going to develop a deep learning based model, we need data to train our model. But we are not going to gather or download any large dataset since this is a simple chatbot. We can just create our own dataset in order to train the model. To create this dataset, we need to understand what are the intents that we are going to train. An “intent” is the intention of the user interacting with a chatbot or the intention behind each message that the chatbot receives from a particular user. According to the domain that you are developing a chatbot solution, these intents may vary from one chatbot solution to another. Therefore it is important to understand the right intents for your chatbot with relevance to the domain that you are going to work with. Then why it needs to define these intents? That’s a very important point to understand. In order to answer questions, search from domain knowledge base and perform various other tasks to continue conversations with the user, your chatbot really needs to understand what the users say or what they intend to do. That’s why your chatbot needs to understand intents behind the user messages (to identify user’s intention).
Finland’s plan aims to use the EDIH-model to strengthen the digital capabilities of companies across the country. The central strategy is based on helping Finnish SME enterprises to easily access, exploit and profit from the more extensive use of business impacting technologies such as artificial intelligence (AI), robotics and high-performance computing. The design constriction of the Finnish NDIH plan means it can link directly in to the EU’s EDIH knowledge base and network. The prospective Finnish hubs will have the built-in ability to accelerate the digital transformation not just in Finland but also on a wider EU level. Earlier this year, the Finnish government rolled-out an open survey scheme to measure interest from the country’s technology sector in these network projects. The survey was designed to help determine which Finnish tech actors may be qualified to join the hubs. The results from the information survey will amplify the Finnish government ministry’s ability to develop a national framework. Moreover, it will allow it to organise and complete an application round for Finland’s candidates to the Eurpean-wide hub by year-end 2020.
Cybersecurity professionals with cloud security skills can gain a $15,025 salary premium by capitalizing on strong market demand for their skills in 2021. DevOps and Application Development Security professionals can expect to earn a $12,266 salary premium based on their unique, in-demand skills. 413,687 job postings for Health Information Security professionals were posted between October 2019 to September 2020, leading all skill areas in demand. Cybersecurity's fastest-growing skill areas reflect the high priority organizations place on building secure digital infrastructures that can scale. Application Development Security and Cloud Security are far and away from the fastest-growing skill areas in cybersecurity, with projected 5-year growth of 164% and 115%, respectively. This underscores the shift from retroactive security strategies to proactive security strategies. According to The U.S. Bureau of Labor Statistics' Information Security Analyst's Outlook, cybersecurity jobs are among the fastest-growing career areas nationally. The BLS predicts cybersecurity jobs will grow 31% through 2029, over seven times faster than the national average job growth of 4%.
There are multiple challenges that can be divided across test automation creation and maintenance, test reporting and analysis, test management, testing trends, and debugging. Traditional tools are not efficient enough to provide practitioners with reliable, robust, and maintainable test scripts. Test automation scripts keep breaking upon developers’ code changes made to the apps, or elements on the app that aren’t properly recognized by the test automation framework. Ongoing maintenance of scripts is also a challenge that causes lots of false negatives and noise that drills into the CI pipeline. As test execution scales, large test data accumulates and needs to be sliced and diced to find the most relevant issues. Here, traditional tools are limited in filtering big test data and providing data-driven smart decisions, trends, root cause of failures, and more. Lastly, the time it takes to create a new script that is code based, and debug it, is way too long to fit into today’s aggressive timelines. Hence, AI and ML are in a great position to close this gap by automatically generating test code and maintaining it through self-healing methods.
The first crucial step is to get hold of the data in a format that will help you understand how money is being spent. This will allow you to put guard rails up to protect against overspending. To do this, you’ll need to utilise tools that break down the usage data. Otherwise, you will just receive a bill that looks like a long shopping list – that is almost impossible to decipher. AWS does offer tools that can help here, but you can also add third party solutions like CloudCheckr to collate this information and play it back to you, with actionable metrics. This will also alert you to any under used resources that could be turned off. You will also want to ensure you implement a consistent tagging strategy across all of your cloud estate. This will allow you to break spending down to the individual customer, department, team and even developer. It will also help you understand where you’re getting a good return on investment and where you might need to be more efficient. It’s essential that you go native in public cloud infrastructures – otherwise you will miss key features that allow you to automate and orchestrate. For example, you should be taking advantage of features that will automate day to day management tasks, such as software updates and back-ups.
Using outside talent to improve productivity and results with data and AI technology is definitely a valid path in the short-run, especially as most banks and credit unions play catch-up in the race to leverage data insights across the organization. But, as mentioned, the ability to deploy the insights to specific product and service needs requires the experience of those who have known the business for years. Without the involvement of the users of the data and AI results, technology is deployed in a vacuum. Marketing managers need to understand the targeting and personalization methodology the models create. Product managers must understand the changes to processes and procedures that is recommended by AI technologies to ensure all of the required steps are in place for compliance purposes. And, risk managers need to feel comfortable that the assumptions made by models continue to reflect the cybersecurity requirements of the organization. The need to upgrade the skills of the consumers of data and AI solutions usually is done by training existing employees of the organization. This is usually a much more efficient and less disruptive process than trying to train technology people the internal intricacies of an organization.
Looking at what the public cloud providers offer in terms of a control plane for managing workloads, McQuire says Azure focuses on hybrid to edge and on-premise workload management. “Google Cloud has been a latecomer in the enterprise and going full board with cloud management,” he adds. For the moment, says McQuire, the focus is on orchestration and control, security and governance. He says this is a reflection of where IT organisations are in terms of how they are using multiple public clouds. “There is a need to understand the economic impact of moving workloads around,” he says. “Not only do you have a need to understand the performance of different IT environments, whether to deploy on-premise, in a private cloud or use one of the three public clouds, there is also a requirement to understand the economics associated with those decisions.” It is now not uncommon for IT decision-makers to standardise on one public cloud for specialist workloads such as artificial intelligence (AI), and use another for infrastructure as a service (IaaS). McQuire adds: “Two years ago, companies started running machine learning workloads with a single cloud provider. ..."
To safeguard user data, organizations are adopting a zero-trust security model. “Zero trust security means that we’re not trusting anybody,” said Palladino. “We don’t trust our own services. We don’t trust our own team members.” Placing too much trust in users, services and teams could cause a catastrophic failure. And, “there is no bigger risk than thinking you are secure, while in reality, you are not,” he said. ... Implementing these permissions is where a service mesh comes in. The application teams often build security, yet it’s generally bad practice to build your own cybersecurity. Security for microservices requires high expertise, and standardizing connectivity between various microservices can easily result in fragmented security implementations. Instead of building your own security infrastructure, Palladino recommended utilizing service mesh. By using service mesh as a control plane for microservices, platform architects can specify specific rules and attributes to generate an identity on a per-service basis. Service mesh also removes the burden of networking from developers, enabling them to focus more on their core logic. “Application teams become consumers of connectivity, as opposed to the makers to this connectivity,” Palladino noted.
Quote for the day:
"You can't use up creativity. The more you use, the more you have." -- Maya Angelou