The level of sophistication of attacks has increased manifold in the past couple of years. Attackers leveraging advanced technology to infiltrate company networks and gain access to mission-critical assets. Given this scenario, organizations too need to leverage futuristic technology such as next-gen WAF, intelligent automation, behavior analytics, deep learning, security analytics, and so on to prevent even the most complex and sophisticated attacks. Automation also enables organizations to gain speed and scalability in the broader IT environment with ramped-up attack activity. Security solutions like Indusface's AppTrana enable all this and more. ... Remote work is here to stay, and the concept of the network perimeter is blurring. For business continuity, organizations have to enable access of mission-critical assets to employees wherever they are. Employees are probably accessing these resources from personal, shared devices and unsecured networks. CISOs need to think strategically and implement borderless security based on a zero-trust architecture.
Cloud computing management raises many information systems management issues that include ethical (security, availability, confidentiality, and privacy) issues, legal and jurisdictional issues, data lock-in, lack of standardized service level agreements (SLAs), and customisation technological bottlenecks, and others. Sharing a cloud provider has some associated risks. The most common cloud security issues include unauthorized access through improper access controls and the misuse of employee credentials. According to industry surveys, unauthorized access and insecure APIs are tied for the No. 1 spot as the single biggest perceived security vulnerability in the cloud. Others include internet protocol vulnerabilities, data recovery vulnerability, metering, billing evasion, vendor security risks, compliance and legal risks, and availability risks. When you store files and data in someone else's server, you're trusting the provider with your crown jewels. Whether in a cloud or on a private server, data loss refers to the unwanted removal of sensitive information, either due to an information system error or theft by cybercriminals.
Site Reliability Engineering, or SRE, is a strategy that uses principles rooted in software engineering to make systems as reliable as possible. In this respect, SRE, which was made popular by Google starting in the mid-2000s, facilitates a shared mindset and shared tooling between software development and IT operations. Instead of writing software using one set of strategies and tools, then managing it using an entirely different set, SRE helps to integrate each practice together by orienting both around concepts rooted in software engineering. Meanwhile, DevOps is a philosophy that, at its core, encourages developers and IT operations teams to work closely together. The driving idea behind DevOps is that when developers have visibility into the problems IT operations teams experience in production, and IT operations teams have visibility into what developers are building as they push new application releases down the development pipeline, the end result is greater efficiency and fewer problems for everyone.
The technical requirements for two-phase commit are that you need a distributed transaction manager such as Narayana and a reliable storage layer for the transaction logs. You also need DTP XA-compatible data sources with associated XA drivers that are capable of participating in distributed transactions, such as RDBMS, message brokers, and caches. If you are lucky to have the right data sources but run in a dynamic environment, such as Kubernetes, you also need an operator-like mechanism to ensure there is only a single instance of the distributed transaction manager. The transaction manager must be highly available and must always have access to the transaction log. For implementation, you could explore a Snowdrop Recovery Controller that uses the Kubernetes StatefulSet pattern for singleton purposes and persistent volumes to store transaction logs. In this category, I also include specifications such as Web Services Atomic Transaction (WS-AtomicTransaction) for SOAP web services.
Today’s threat detection solutions use a combination of signatures, heuristics, and machine learning for anomaly detection. The problem is that they do this on a tactical basis by focusing on endpoints, networks, or cloud workloads alone. XDR solutions will include these tried-and-true detection methods, only in a more correlated way on layers of control points across hybrid IT. XDR will go further than existing solutions with new uses of artificial intelligence and machine learning (AI/ML). Think “nested algorithms” a la Russian dolls where there are layered algorithms to analyze aberrant behavior across endpoints, networks, clouds, and threat intelligence. Oh, and it kind of doesn’t matter which security telemetry sources XDR vendors use to build these nested algorithms, as long as they produce accurate high-fidelity alerts. This means that some vendors will anchor XDR to endpoint data, some to network data, some to logs, and so on. To be clear, this won’t be easy: Many vendors won’t have the engineering chops to pull this off, leading to some XDR solutions that produce a cacophony of false positive alerts.
First, public key cryptography was not designed for a hyper-connected world, it wasn't designed for an Internet of Things, it's unsuitable for the nature of the world that we're building. The need to constantly refer to certification providers for authentication or verification is fundamentally unsuitable. And of course the mathematical primitives at the heart of that are definitely compromised by quantum attacks so you have a system which is crumbling and is certainly dead in a few years time. A lot of the attacks we've seen result from certifications being compromised, certificates expiring, certificates being stolen and abused. But with the sort of computational power available from a quantum computer blockchain is also at risk. If you make a signature bigger to guard against it being cracked the block size becomes huge and the whole blockchain grinds to a halt. Think of the data centers as buckets, three times a day the satellites throw some random numbers into the buckets and all data centers end up with an identical bucket full of identical sets of random information.
Despite the complexity and lengthy time horizon of a holistic effort to modernize the data landscape, governments can establish and sustain a focus on rapid, tangible impact. A failure to deliver results from the outset can undermine stakeholder support. In addition, implementing use cases early on helps governments identify gaps in their data landscapes (for example, useful information that is not stored in any register) and missing functionalities in the central data-exchange infrastructure. To deliver impact quickly, governments may deploy “data labs”—agile implementation units with cross-functional expertise that focus on specific use cases. Solutions are rapidly developed, tested, iterated and, once successful, rolled out at scale. The German government is pursuing this approach in its effort to modernize key registers and capture more value. ... Organizations such as Estonia’s Information System Authority or Singapore’s Government Data Office have played a critical role in transforming the data landscape of their respective countries.
AI researchers base their systems on two types of inference machines: deductive and inductive. Deductive inference uses prior knowledge to reason about the world. This is the basis of symbolic artificial intelligence, the main focus of researchers in the early decades of AI. Engineers create symbolic systems by endowing them with a predefined set of rules and facts, and the AI uses this knowledge to reason about the data it receives. Inductive inference, which has gained more traction among AI researchers and tech companies in the past decade, is the acquisition of knowledge through experience. Machine learning algorithms are inductive inference engines. An ML model trained on relevant examples will find patterns that map inputs to outputs. In recent years, AI researchers have used machine learning, big data, and advanced processors to train models on tasks that were beyond the capacity of symbolic systems. A third type of reasoning, abductive inference, was first introduced by American scientist Charles Sanders Peirce in the 19th century.
Nothing is more frustrating as a code reviewer than reviewing someone else’s code who clearly didn’t do these checks themselves. It wastes the code reviewer’s time when he has to catch simple mistakes like commented out code, bad formatting, failing unit tests, or broken functionality in the code. All of these mistakes can easily be caught by the code author or by a CI pipeline. When merge requests are frequently full of errors, it turns the code review process into a gatekeeping process in which a handful of more senior engineers serve as the gatekeepers. This is an unfavorable scenario that creates bottlenecks and slows down the team’s velocity. It also detracts from the higher purpose of code reviews, which is knowledge sharing. We can use checklists and merge request templates to serve as reminders to ourselves of things to double check. Have you reviewed your own code? Have you written unit tests? Have you updated any documentation as needed? For frontend code, have you validated your changes in each browser your company supports?
Quote for the day:
"Effective leadership is not about making speeches or being liked; leadership is defined by results not attributes." -- Peter Drucker