Many government agencies employ some type of MFA. But the Biden administration's guidelines call for all agencies to implement stronger security. While legacy MFA is more secure than using a username and password, it assumes that using a second device and adding a second factor improves security. It's not that simple Most legacy MFA uses a combination of a password and a "something you have" factor. That "something you have" comes into play when implementing the second factor - a one-time code presented by either a physical token, a text message, or an email sent to the user. But adding a secondary device or channel is, at best, much harder to secure and, at worst, impossible to secure. Phishing campaigns can often phish the additional codes or conduct a man-in-the-middle attack on the authentication sequences, as made clear by recent breaches of the companies Uber and Cisco. The biggest issue, however, is that most MFA solutions rely on shared secrets, like passwords, and provide no security context that ties back to the end user and their device.
To be sure, the question of genuine intelligence does still matter to a handful of thinkers. In the past month, ZDNET has interviewed two prominent scholars who are very much concerned with that question. Yann LeCun, chief AI scientist at Facebook owner Meta Properties, spoke at length with ZDNET about a paper he put out this summer as a kind of think piece on where AI needs to go. LeCun expressed concern that the dominant work of deep learning today, if it simply pursues its present course, will not achieve what he refers to as "true" intelligence, which includes things such as an ability for a computer system to plan a course of action using common sense. LeCun expresses an engineer's concern that without true intelligence, such programs will ultimately prove brittle, meaning, they could break before they ever do what we want them to do. ... The field of AI is undergoing a shift in attitude. It used to be the case that every achievement of an AI program, no matter how good, would be received with the skeptical remark, "Well, but that doesn't mean it's intelligent."
We see the growth of TensorFlow not just as an achievement to celebrate, but as an opportunity to go further and deliver more value for the machine learning community. Our goal is to provide the best machine learning platform on the planet. Software that will become a new superpower in the toolbox of every developer. Software that will turn machine learning from a niche craft into an industry as mature as web development. To achieve this, we listen to the needs of our users, anticipate new industry trends, iterate on our APIs, and work to make it increasingly easy for you to innovate at scale. In the same way that TensorFlow originally helped the rise of deep learning, we want to continue to facilitate the evolution of machine learning by giving you the platform that lets you push the boundaries of what's possible. Machine learning is evolving rapidly, and so is TensorFlow. Today, we're excited to announce we've started working on the next iteration of TensorFlow that will enable the next decade of machine learning development. We are building on TensorFlow's class-leading capabilities, and focusing on four pillars.
Next week, a law takes effect that will change the internet forever—and make it much more difficult to be a tech giant. On November 1, the European Union’s Digital Markets Act comes into force, starting the clock on a process expected to force Amazon, Google, and Meta to make their platforms more open and interoperable in 2023. That could bring major changes to what people can do with their devices and apps, in a new reminder that Europe has regulated tech companies much more actively than the US. “We expect the consequences to be significant,” says Gerard de Graaf, a veteran EU official who helped pass the DMA early this year. Last month, he became director of a new EU office in San Francisco, established in part to explain the law’s consequences to big tech companies. De Graaf says they will be forced to break open their walled gardens. “If you have an iPhone, you should be able to download apps not just from the App Store [but] from other app stores or from the internet,” de Graaf says, in a conference room with emerald green accents at the Irish consulate in San Francisco where the EU’s office is initially located.
It's not surprising that all-in-one pipeline automation has become a holy grail for some platform providers. Many enterprises share the same cloud providers, the same department-level SaaSes, and the same types of de facto-standard databases. The clear logic behind an all-in-one platform like Gathr, for example, is that companies will often need the same connectors or "operators," much of the same drag-and-drop machine learning process assembly, and the same sorts of choices between, ETL, ELT and ingestion capabilities. Unifying all this functionality could mean less work for data and analytics teams. But enterprises should remember that the compulsion to subscribe to yet another SaaS extends to these platforms. Engineers in one business unit might gravitate to a Gathr, while others might favor an Alteryx to map together sources a BI platform might need, or a super SaaS like OneSaaS that allows simplified mixing and matching within the OneSaaS environment.
According to the cybersecurity hype report, confusing marketing strategies by vendors confused most security leaders. Subsequently, 91% of decision-makers found it difficult to select cybersecurity vendors due to unclear marketing about their specific offerings. Additionally, 49% of security leaders said their organization suffers from vendor sprawl, resulting in an increased attack surface. Consequently, 92% of organizations implement a defense-in-depth strategy and have to manage between 10 and 30 different security products. Defense-in-depth aims to create more technological layers to detect, prevent, contain, remediate, and recover from attacks. In a noisy marketplace filled with unsubstantiated claims, users cannot accurately predict the effectiveness of the hyped solutions, nor do they have the time to do so. ... “Buyers are faced with a crowded and complex market, needing to continually layer new security products into their environment to achieve defense-in-depth, assess new and emerging AI technologies, and continually re-invest in SA&T.”
The first step in thinking for oneself is self-awareness. When you understand your values, motives, and aspirations, thinking becomes automatic. Knowing your strengths and weaknesses, you can selectively apply the knowledge you gained by reading or the wisdom of others. Thinking for oneself doesn’t mean you ignore all the knowledge you have gained on the subject. Instead, you question what your current knowledge tells you. Cultivate your thinking using mental models, which explain how things work. James Clear, the author of the best-seller, Atomic Habits, describes many mental models in his blog “Mental Models: Learn How to Think Better and Gain a Mental Edge.” One of these mental models is inversion. An example of the application of inversion is to assume your most crucial project has failed six months from now and ask yourself how it could have failed. Such an exercise gives you all the things you need to look out for and plan to mitigate them for the project’s success. Thinking and doing go hand in hand. Put your thinking into action. Take the learning and refine your knowledge.
Being able to secure your cloud service supply not only requires data controls, but also access to legal controls. As such, hyperscalers have started adapting how they deploy cloud services to give nation states assurance — essentially meaning that cloud services are deployed in partnership with a local organisation. This has given a rise to sovereign partnerships that license the hyperscaler technology, and are delivered by suppliers under the local legal framework. This pragmatic approach has slowly become more common in recent months, and helps overcome many of the risks associated with using cloud, particularly its assurance of service supply. Despite this, one of the biggest barriers to cloud is the current regulatory landscape surrounding how certain sectors need to control data sovereignty and how that data is securely processed. This often requires a long list of requirements that must be fulfilled to shift services onto the cloud, which is unique for each industry.
The arguments for OSS on the mainframe are in many cases the same as for OSS on any other platform -- more accessible, often more secure, easier to develop. “These arguments are from the same development teams who push for OSS elsewhere in the environment,” says Mike Parkin, senior technical engineer at Vulcan Cyber. “The major differences are when the implementation is specific to the mainframe environment.” ... Parkin adds there has been a trend to use mainframe platforms for virtualization, essentially replacing a rack of commodity class servers with a single Big Iron machine that can do the job more efficiently and effectively. “Those are ideal use cases for open-source software at multiple levels, from the guest operating systems to the application layers,” he says. Boris Cipot, senior security engineer at Synopsys Software Integrity Group, a provider of integrated software solutions, agrees that open source can bring fresher and better integrations into today’s working processes and tools, and enable companies to focus on their work and not re-create existing software functionality.
Unfortunately, the challenges many organizations face include narrowing down which intelligence sources they’re pulling from, how many can be leveraged at a time, and how they’re integrated into firewalls and other security solutions. No one source of threat intelligence or existing security control can successfully cover the entirety of the threat landscape. It is critical for organizations to deploy threat intelligence from multiple sources, even those that traditionally would compete with one another. These can include commercial providers, open source intelligence data, government agencies and industry sources—all working together to provide organizations with visibility into the traffic affecting their networks. The data is in and the results are clear: What we don't know in the cybersecurity world can hurt us. Thankfully, there are steps your organization—regardless of size—can take to help ensure your network, users and data are protected.
Quote for the day:
"You may be good. You may even be better than everyone esle. But without a coach you will never be as good as you could be." -- Andy Stanley