A data lake is a place for storing large amounts of data that originate from various sources and are stored in their raw form. Important is, the heterogeneous data is neither cleaned nor transformed before the loading process. After the loading process is complete, the data is now available in a single system. In addition to structured data, a data lake also stores and manages semi-structured (CSV, logs, JSON), unstructured (e-mails, documents, reports), and binary data (video, audio, images). The list of all possible formats is of course incomplete, but I think you know what I mean. The goal is to gather all company data in one place in order to be able to quickly access the entire data stock. Users should be able to immediately create visualizations, reports, and analyses from the data. ... In order for the principle of the data lake to work efficiently for you and not result in a data swamp in which no more data can be found, the collected data must show a business added value for the future. It is very difficult for analysts to extract information from the volume of data. This is especially true when there is no metadata or tags used. Without this, it is hardly possible for analysts to assign the data.
The concept of policies replacing security standards builds on the idea of culture shifts. Security standards are typically just a piece of documentation saved on Confluence or GSuite somewhere. They may get examined by a developer during a mandatory annual training session, or occasionally for reference, but they aren’t dynamic and are rarely top of mind. Those responsible for enforcing such standards are normally compliance or security operations specialists, who are logically distanced from developers. Aside from low adoption rates and disruptions to Agile workflows, security standards often lead to the ‘enforcer’ becoming the bad guy. This pushes even more of a wedge between dev and security, making security feel a bit like doing your taxes (and no one wants that). If the expertise of the traditional ‘enforcer’ is shared with developers and dynamic, adaptable policies are adopted in place of rigid standards, then security simply becomes part of the workflow. Zero-trust networking is a great example of this. Zero-trust networking is probably the best way to secure your infrastructure, and it relies on expertly defined and managed policies being present through each of its 10 principles.
Future success relies on leaders’ digital ability as much as their aptitude for uniting teams and encouraging people to embrace new technology and new ways of working at every level of the organisation. Without the leadership to make new systems and processes work and deliver against business objectives, outlay in innovation quickly becomes a source of future technical debt. Developing leadership behaviours including emotional intelligence (EQ) will help CIOs to build empathy; understanding the human impact of transition ensures teams feel heard and valued, reduce resistance to change and enables CIOs to build trust. Equally, strong communication skills will enable CIOs to speak in both the languages of data and business and use storytelling to share their vision and secure buy-in from teams and shareholders. CIOs are also protectorates. With greater concern over business threats, CIOs safeguard their organisations’ assets and future. As well as managing data governance and cyber security, they can add business value by anticipating the opportunities and risks presented by disruption.
One of the more complex vishing schemes is the man-in-the-middle attack, in which a fraudster sets up two parallel conversations between a business and its customer. The business believes it is connecting with the customer, and the customer thinks they are talking to the business — but in reality, it is the fraudster interacting with both. The fraudster might initiate the scheme by requesting the issuance of a one-time passcode via a session on the business’s website. In parallel, posing as the business, the fraudster calls the unwitting customer and, using social engineering, convinces the individual to read off the one-time passcode sent by the business. The fraudster then uses this information to log in to the customer’s account and perform unauthorized transactions. Since the fraudster was able to provide all requested data to pass each point in the verification process, access is granted. With synthetic identity fraud, criminals combine real and fake information to create a fictitious identity, which they use to open up financial accounts and make fraudulent purchases. While a false identity might seem easy to spot, the reality is much more challenging.
Given the distributed nature of serverless functions – essentially, the reason for its flexibility and scalability – many existing security tools will provide little to no visibility, nor the control capabilities, for these computing environments. Many of the security attacks that will occur in serverless functions will be a result of misconfigurations and mistakes that happen outside the purview of the security team and due to legacy solutions which don’t translate to serverless architectures. Further, because abstracted workloads create blind spots, attackers will have more room to maneuver undetected. Serverless functions will even render some traditional DevSecOps tools less useful. Scanning tools must monitor hundreds of individual repositories instead of a single monolithic repository, while application performance monitoring (APM) tools lack security proficiency and cannot protect from the OWASP Serverless Top 10 risks. ... For many organizations, serverless architecture is a very different and unique computing environment – unlike anything they’ve experienced or had to protect before now. That reality means that organizations need a fresh approach to securing these environments and will need to look beyond the traditional tools they have in their tech stack today.
The Center for Internet Security has updated its set of safeguards for warding off the five most common types of attacks facing enterprise networks—web-application hacking, insider and privilege misuse, malware, ransomware, and targeted intrusions. In issuing its CIS Controls V8 this month, the organization sought to present practical and specific actions businesses can take to protect their networks and data. These range from making an inventory of enterprise assets to account management to auditing logs. In part the new version was needed to address changes to how businesses operate since V7 was issued three years ago, and those changes guided the work. “Movement to cloud-based computing, virtualization, mobility, outsourcing, work-from-home, and changing attacker tactics have been central in every discussion,” the new controls document says. CIS changed the format of the controls a bit, describing actions that should be taken to address threats and weaknesses without saying who should perform those tasks. That put the focus on the tasks without tying them to specific teams within the enterprise. The controls each come with detailed procedures for implementing them along with links to related resource.
The key to successful digital banking transformation includes embracing the cloud. While there have been reservations in the past around cloud security and regulation, cloud computing solutions are becoming prevalent in the marketplace for both traditional and non-traditional financial institutions. The use of data and deployment of advanced analytics, machine learning, and artificial intelligence requires more processing power than all but the largest financial institutions posses. The good news is that there are several cloud-based solution providers, like IBM, that have created industry-specific solutions for the banking industry. According to IBM, “Organizations have an enormous opportunity to leverage cloud computing to drive innovation and improve their competitive position. Cloud computing – whether private, hybrid or public – enables organizations to be far more agile while reducing IT costs and operational expenses. In addition, cloud models enable organizations to embrace the digital transformation necessary to remain competitive in the future.”
Smart technology itself, increasingly being deployed across government and private sector systems, may soon create new webs of vulnerability, according to a number of leading cybersecurity researchers. The problem is twofold: Hackers will ultimately begin using artificial intelligence against systems and there is concern that an inability to quickly spot flaws in machine learning models could create even more vulnerabilities. It would be naïve to believe that criminal hackers, who have already built help desk support operations and a vast marketplace for “plug and play” intrusion tools, would not find a way to use AI for attacks. “My guess is this isn’t very far off, and we had better start thinking about its implications,” said security technologist Bruce Schneier. “As AI systems get more capable, society will cede more and more important decisions to them, which means that hacks of those systems will become more damaging.” There is also concern within the cybersecurity community that growing use of machine learning could be opening new avenues of exploit for threat actors. Adi Shamir, professor at the Weizmann Institute in Rehovot, Israel, and a co-founder of RSA, has been analyzing the fragile state of neural networks and recently published a paper on his findings.
There are two issues with data collection. The first is proper instrumentation. It sounds easier than it is. The entire observability, monitoring, and AIOps eco-system depend on properly instrumenting your observable sources. If your systems, devices, services, and infrastructure is not properly instrumented, then you will have data blind spots. No matter how much data you collect from certain areas, if you do not have a holistic view of all the telemetry components, you will be getting a partial view of any system. Obviously, the instrumentation depends mostly on developers. The second issue is integration. As any AIOps vendor will tell you, this probably is the most difficult part to get your AIOps solution going. The more input from varying telemetry sources, the better the insights will be. Any good AIOps solution will be able to integrate easily with the basic golden telemetry – logs, metrics, and traces. In addition, integrating with notification systems (such as OpsGenie, Pagerduty, etc.), and maybe event streams (such as Kafka, etc.) is useful as well. However, I quite often see major enterprises struggling a lot to integrate the AIOps solutions with their existing enterprise systems.
An integral approach incorporates all of the essential perspectives, schools of thought, and methods into a unified, comprehensive and accurate framework" is a simple definition from the book. The main leverage of Integral Theory is that it provides a meta-framework for mapping other techniques, approaches, and frameworks onto. The fundamental premise of integral thinking is that any school of thought or method that has been around for any length of time must have some truth to it -- "all perspectives are true, but partial" is a Wilber quote. Integral helps us take multiple perspectives on situations, which is key for change and adaptability in a complex world, instead of getting stuck in our own, limited perspective. As Ken Wilber said to us when we interviewed him for the book -- there are two things that, above all else, make real transformation possible -- the ability to take the perspective of others, and the ability to see one’s own "seer". Both of these are fostered by using integral thinking. Doing this cuts through our confusion when we run into the challenges of existing culture and leadership mindsets when implementing agile.
Quote for the day:
"Open Leadership: the act of engaging others to influence and execute a coordinated and harmonious conclusion." -- Dan Pontefract