At its core, confidential computing encrypts data at the hardware level. It’s a way of “protecting data and applications by running them in a secure, trusted environment,” explains Noam Dror—SVP of solution engineering at HUB Security, a Tel Aviv, Israel-based cybersecurity company that specializes in confidential computing. In other words, confidential computing is like running your data and code in an isolated, secure black box, known as an “enclave” or trusted execution environment (TEE), that’s inaccessible to unauthorized systems. The enclave also encrypts all the data inside, allowing you to process your data even when hackers breach your infrastructure. Encryption makes the information invisible to human users, cloud providers, and other computer resources. Encryption is the best way to secure data in the cloud, says Kurt Rohloff, cofounder and CTO at Duality, a cybersecurity firm based in New Jersey. Confidential computing, he says, allows multiple sources to analyze and upload data to shared environments, such as a commercial third-party cloud environment, without worrying about data leakage.
Many legacy MFA platforms rely on easily phishable factors like passwords, push notifications, one-time codes, or magic links delivered via email or SMS. In addition to the complicated and often frustrating user experience they create, phishable factors such as these open organizations up to cyber threats. Through social engineering attacks, employees can be easily manipulated into providing these authentication factors to a cyber criminal. And by relying on these factors, the burden to protect digital identities lies squarely on the end user, meaning organizations’ cybersecurity strategies can hinge entirely on a moment of human error. Beyond social engineering, man-in-the middle attacks and readily available toolkits make bypassing existing MFA a trivial exercise. Where there is a password and other weak and phishable factors, there is an attack vector for hackers, leaving organizations to suffer the consequences of account takeovers, ransomware attacks, data leakage, and more. A phishing-resistant MFA solution completely removes these factors, making it impossible for an end user to be tricked into handing them over even by accident or collected by automated phishing tactics.
While the UK government has tried to recognise the importance of digital supply chain security, current policy doesn’t consider open source as part of that supply chain. Instead, regulation or proposed policies focus only on third-party software vendors in the traditional sense but fail to recognise the building blocks of all software today and the supply chain behind it. To hammer the point, the UK’s 11,000+ word National Cyber Security Strategy does not include a single reference to open source. GCHQ guidance meanwhile remains limited, with little detailed direction beyond ‘pull together a list of your software’s open source components or ask your suppliers.’ ... In this sense, the EU has certainly been listening. The recently released Cyber Resilience Act (CRA) is its proposed regulation to combat threats affecting any digital entity and ‘bolster cyber security rules to ensure more secure hardware and software products’. First, the encouraging bits: the CRA doesn’t just call for vendors and producers of software to have (among other things) a Software Bill of Materials (SBoM) - it demands companies have the ability to recall components.
Lack of data culture: Data hidden within silos with little communication between business units leads to a lack of data culture. Data Literacy and enterprise-wide data training is required to allow business staff to read, analyze, and discuss data. Data culture is the starting point for developing an effective Data Strategy.The Data Strategy is too focused on data and not on the business side of things: When businesses focus too much on just data, the Data Strategy may just end up serving the needs of analytics without any focus on business needs. An ideal Data Strategy enlists human capabilities and provides opportunities for training staff to carry out the strategy to meet business goals. This approach will work better if citizen data scientists are included in strategy teams to bridge the gap between the data scientist and the business analyst.Investing in data technology before democratizing data: In many cases, Data Strategy initiatives focus on quick investment in technology without first addressing data access issues. If data access is not considered first, costly technology investments will go to waste.
Every data science project needs to start with an evaluation of your primary goals. What opportunities are there to improve your core competency? Are there any specific questions you have about your products, services, customers, or operations? And is there a small and easy proof of concept you can launch to gain traction and master the technology? The above use case from GE is a prime example of having a clear goal in mind. The multinational company was in the middle of restructuring, re-emphasizing its focus on aero engines and power equipment. With the goal of reducing their six- to 12-month design process, they decided to pursue a machine learning project capable of increasing the efficiency of product design within their core verticals. As a result, this project promises to decrease design time and budget allocated for R&D. Organizations that embody GE's strategy will face fewer false starts with their data science projects. For those that are still unsure about how to adapt data-driven thinking to their business, an outsourced partner can simplify the selection process and optimize your outcomes.
The role of a data manager in an organization is tricky. This person is often neither an IT guy who implements databases on his/her own, nor a business guy who is actually responsible for data or processes (that’s rather a Data Steward’s area of responsibility). So what’s the real value-add of a data manager (or even a data management department)? In my opinion, you need someone who is building bridges between the different data stakeholders on a methodical level. It’s rather easy to find people who consider themselves as experts for a particular business area, data analysis method or IT tool, but it is rather complicated to find one person who is willing to connect all these people and to organize their competencies as it is often required in data projects. So what I am referring to are skills like networking, project management, stakeholder management and change management HIwhich are required to build a data community step-by-step as backbone for Data Governance. Without people, a data manager will fail! So in my opinion, a recruiter who seeks for data managers should not only challenge technical skills but also these people skills.
There is nonetheless an expectation that DLT can prove to be a net good for financial markets. Foreign exchange markets have an estimated $8.9 trillion at risk every day due to the final settlement of transactions between two parties taking days. This is why the Financial Stability Board and the Committee on Payments and Market Infrastructures have focused their efforts on enhancing cross-border payments with a comprehensive global roadmap. Part of this roadmap includes exploring the use of DLT and Central Bank Digital Currencies. The problem may not be the technology itself, but the aim of replacing current technology systems with distributed networks. DLT networks are being designed to completely overhaul and replace legacy technology that financial markets depend on today. Many pilot projects, such as mBridge and Jura, rely on a single blockchain developed by a single vendor. This introduces a single point of trust, and removes many of the benefits of disintermediation.
The information architecture within a design (both process and output) makes the balancing within the equation possible. It also ensures the equation is “solvable” by other people. It does this by introducing logical coherence. It ensures words, images, shapes and colours are used consistently. And it ensures that as we move from idea to execution, we stay true to the original intent — and can clearly articulate it — so that we can meaningfully measure the effectiveness of our design. Without this internal coherence and confidence that our output is an accurate, reliable test of our hypothesis, we’re not doing design. The power of design which has a consistent information architecture is that if we find that our idea (which we translate to intent, experiments and experiences) is not equal to the problem, we can interrogate every part of the equation. We may have made a mistake in execution. Maybe our idea wasn’t quite right. Or even more powerfully, maybe we didn’t really understand the problem fully.
You can improve your software quality with a strong digital immune system since a digital immune system is designed to guard against cyberattacks and other sorts of hostile activities on computer systems, networks, and hardware. It operates by constantly scanning the network and systems for indications of prospective threats and then taking the necessary precautions to thwart or lessen such dangers. This can entail detecting and preventing malicious communications, identifying and containing compromised devices, and patching security holes. A robust digital immune system should offer powerful and efficient protection against cyber threats and assist individuals and companies in staying secure online. Experts in software engineering are searching for fresh methods and strategies to reduce risks and maximize commercial impact. The idea of “digital immunity” offers a direction. It consists of a collection of techniques and tools for creating robust software programmes that provide top-notch user experiences. With the help of this roadmap, software engineering teams may identify and address a wide range of problems, including functional faults, security flaws, and inconsistent data.
For each one of the types of testing listed above, a different skillset is required. All of them require patience, attention to detail, basic technical skills, and the ability to document what you have found in a way that the software developers will understand and be able to fix the issue(s). That is where the similarities end. Each one of these types of testing requires different experience, knowledge, and tools, often meaning you need to hire different resources to perform the different tasks. Also, we can’t concentrate on everything at once and still do a great job at each one of them. Although theoretically you could find one person who is both skilled and experienced in all of these areas, it is rare, and that person would likely be costly to employ as a full-time resource. This is one reason that people hired for general software testing are not often also tasked with security testing. Another reason is that people who have the experience and skills to perform thorough and complete security testing are currently a rarity.
Quote for the day:
"Leadership is particularly necessary to ensure ready acceptance of the unfamiliar and that which is contrary to tradition." -- Cyril Falls