In general, zero-trust initiatives have two goals in mind: reduce the attack surface and increase visibility. To demonstrate this, consider the (common) scenario of a ransomware gang buying initial access to a company’s cloud through an underground initial-access broker and then attempting to mount an attack. In terms of visibility, “zero trust should stop that attack, or make it so difficult that it will be spotted much earlier,” said Greg Young, vice president of cybersecurity at Trend Micro. “If companies know the postures of their identities, applications, cloud workloads, data sources and containers involved in the cloud, it should make it exceedingly hard for attackers. Knowing what is unpatched, what is an untrusted lateral movement, and continuously monitoring the posture of identities really limits the attack surface available to them.” And on the attack-surface front, Malik noted that if the gang used a zero-day or unpatched vulnerability to gain access, zero trust will box the attackers in. “First, at some point the attackers will cause a trusted user or process to begin misbehaving,” he explained.
Expert adversaries, often called Advanced Persistent Threats (APTs), are the boogeymen of security. Their motivations and capabilities vary widely, but they tend to be well-heeled and, as the moniker suggests, persistent; unfortunately, it’s likely they will always be around. Different APTs run many different types of operations, but these threat actors tend to be the likeliest to attack the network layer of companies directly to accomplish their goals. We know some advanced groups are actively targeting web3 projects, and we suspect there are others who have yet to be identified. ... One of the most well-known APTs is Lazarus, a North Korean group which the FBI recently attributed as having conducted the largest crypto hack to date. ... Now that web3 lets people directly trade assets, such as tokens or NFTs, with almost instant finality, phishing campaigns are targeting its users. These attacks are the easiest way for people with little knowledge or technical expertise to make money stealing crypto. Even so, they remain a valuable method for organized groups to go after high-value targets, or for advanced groups to wage broad-based, wallet-draining attacks through, for example, website takeovers.
In light of the changes in the nature of data, the level of data regulation, and the data democratization trend, it’s safe to say that the traditional, old, boring, data governance is dead. We can’t let it in the grave, as we need data governance more than ever today. Our job is thus to resurrect it, and give it a new face. ... Data governance should embrace the trends of operational analytics and data democratization, and ensure that anybody can use data at any time to make decisions with no barrier to access or understanding. Data democratization means that there are no gatekeepers creating a bottleneck at the gateway to data. This is worth mentioning, as the need for data governance to be secure and compliant often leads programs to create bottlenecks at the gateway to data, as the IT team is usually put in charge of granting access to data. Operational people can end up waiting hours until they manage to get access to a dataset. By then, they have already given up on their analysis. It’s important to have security and control, but not at the expense of the agility that data offers.
Companies need to understand what’s possible in the metaverse, what’s already in use and what customers or employees will expect as more organizations create immersive experiences to differentiate their products and services. The possibilities may include improvements in what companies are doing now as well as revolutionary changes in the way companies operate, connect and engage with customers and employees to increase loyalty. How can leaders start to identify opportunities in the metaverse? Start, as always, with low-hanging fruit, like commerce and brand experiences that can benefit from immersive support. Also consider the technology that can enable what you need. From an architectural standpoint, it’s helpful to think of immersive experiences as a three-layer cake. The top layer is where users get access via systems of engagement. The middle layer is where messages are sent, received and routed to the right people via systems of integration. The bottom layer comprises the databases and transactions — the systems of record.
The problem is that if there’s a predictable difference between two groups on average, then these two definitions will be at odds. If you design your search engine to make statistically unbiased predictions about the gender breakdown among CEOs, then it will necessarily be biased in the second sense of the word. And if you design it not to have its predictions correlate with gender, it will necessarily be biased in the statistical sense. So, what should you do? How would you resolve the trade-off? Hold this question in your mind, because we’ll come back to it later. While you’re chewing on that, consider the fact that just as there’s no one definition of bias, there is no one definition of fairness. Fairness can have many different meanings — at least 21 different ones, by one computer scientist’s count — and those meanings are sometimes in tension with each other. “We’re currently in a crisis period, where we lack the ethical capacity to solve this problem,” said John Basl, a Northeastern University philosopher who specializes in emerging technologies. So what do big players in the tech space mean, really, when they say they care about making AI that’s fair and unbiased?
Indeed, QC makes use of an uncanny quality of quantum mechanics whereby an electron or atomic particle can be in two states at the same time. In classical computing, an electric charge represents information as either an 0 or a 1 and that is fixed, but in quantum computing, an atomic particle can be both a 0 and a 1, or a 1 and a 1, or a 0 and a 0, etc. If this unique quality can be harnessed, computing power explodes manyfold, and QC’s development, paired with Shor’s algorithm — first described in 1994 as a theoretical possibility, but soon to be a wide-reaching reality, many believe — also threatens to burst apart RSA encryption, which is used in much of the internet including websites and email. “Yes, it’s a very tough and exciting weapons race,” Miyano told Cointelegraph. “Attacks — including side-channel attacks — to cryptosystems are becoming more and more powerful, owing to the progress in computers and mathematical algorithms running on the machines. Any cryptosystem could be broken suddenly because of the emergence of an incredibly powerful algorithm.”
Criminals have also shifted their focus from Docker to Kubernetes. Attacks against vulnerable Kubernetes deployments and applications increased to 19% in 2021, up from 9% in 2020. Kubernetes environments are a tempting target, as once an attacker gains initial access, they can easily move laterally to expand their presence. Attacks that affect an entire supply chain have increased over the past few years, and that has been felt across the software supply chain as well. In 2021, attackers aiming at software suppliers as well as their customers and partners employed a variety of tactics, including exploiting open source vulnerabilities, infecting popular open source packages, compromising CI/CD tools and code integrity, and manipulating the build process. Last year, supply-chain attacks accounted for 14.3% of the samples seen from public image libraries. “These findings underscore the reality that cloud native environments now represent a target for attackers, and that the techniques are always evolving,” said Assaf Morag, threat intelligence and data analyst lead for Aqua’s Team Nautilus.
Because a single database server is shared between a variety of client applications, a single rogue transaction from an unoptimized query could potentially modify millions of rows in one of the databases on the server, causing performance implications for the other databases. These transactions have the potential to overload the I/O subsystem and stall the database server. In this situation, the Orchestrator is unable to get a response from the primary node, and the replicas also face issues in connecting to the primary. This causes the Orchestrator to initiate a failover. This problem is compounded by the application re-trying these transactions upon failure, and stalling the database operations repeatedly. These transactions halt the database for many seconds and the Orchestrator is quick to catch the stalled state and initiate a failover, impacting the general availability of the MySQL platform. We knew that MySQL stores the number of rows modified by any running transaction, and this number can be obtained by querying the trx_rows_modified of the innodb_trx table, in the information_schema database.
The bill would “establish much needed, yet reasonable, limitations on how employers use data-driven technology at work,” Kalra told the Assembly Labor and Employment Committee on Wednesday. “The time is now to address the increasing use of unregulated data-driven technologies in the workplace and give workers — and the state — the necessary tools to mitigate any insidious impacts caused by them.” The use of digital surveillance software grew during the pandemic as employers sought to track employees’ productivity and activity when working from home, installing software that uses techniques such as keystroke logging and webcam monitoring. Digital monitoring and management is being used across a variety sectors, with warehouse staff, truck drivers and ride-hailing drivers subject to movement and location tracking for example, with decisions around promotions, hiring and even firing made by algorithms in some cases. The bill, which was approved by the committee on a 5-2 vote and now moves to the Appropriations Committee for more debate, makes three core proposals
It is a mistake to act on laws that apply only in the geographic location of business operations. There might be privacy regulations/compliance issues that apply to a company beyond those that exist where the company is located – for example, a company headquartered in New York might have customers in Europe, and some European data privacy regulations likely would apply beyond any U.S.-based regulations. This is a significant problem with breach response laws. A large number of U.S. organizations follow the requirements only for their own state or territory. There are at least 54 U.S. state/territory breach laws, so this belief could be very costly. Privacy management programs should apply to all applicable laws and regulations of the associated individuals and also synthesize all requirements so that one set of procedures can be followed to address the common requirements, in addition to meeting unique requirements for specific laws. Many organizations are also overconfident that they will not experience a privacy breach, which leaves them unable to respond effectively, efficiently, and fully when a breach does happen.
Quote for the day:
"Leadership is just another word for training." -- Lance Secretan