Once installed on a compromised machine, PowerLess allows attackers to download additional payloads, and steal information, while a keylogging tool sends all the keystrokes entered by the user direct to the attacker. Analysis of PowerLess backdoor campaigns appear to link attacks to tools, techniques and motivations associated with Phosphorus campaigns. In addition to this, analysis of the activity seems to link the Phosphorus threat group to ransomware attacks. One of the IP addresses being used in the campaigns also serves as a command and control server for the recently discovered Momento ransomware, leading researchers to suggest there could be a link between the ransomware attacks and state-backed activity. "A connection between Phosphorus and the Memento ransomware was also found through mutual TTP patterns and attack infrastructure, strengthening the connection between this previously unattributed ransomware and the Phosphorus group," said the report. Cybereason also found a link between a second Iranian hacking operation, named Moses Staff, and additional ransomware attacks, which are deployed with the aid of another newly identified trojan backdoor, dubbed StrifeWater.
Paying down technical debt while maintaining a competitive velocity delivering features can be difficult, and it only gets worse as system architectures get larger. Managing technical debt for dozens or hundreds of microservices is much more complicated than for a single service, and the risks associated with not paying it down grow faster. Every software company gets to a point where dealing with technical debt becomes inevitable. At Optum Digital, a portfolio – also known as a software product line – is a collection of products that, in combination, serve a specific need. Multiple teams get assigned to each product, typically aligned with a software client or backend service. There are also teams for more platform-oriented services that function across several portfolios. Each team most likely is responsible for various software repositories. There are more than 700 engineers developing hundreds of microservices. They take technical debt very seriously because the risks of it getting out of control are very real.
European tech offers a serious alternative to US and Chinese models when it comes to data. It’s also a necessary alternative and must have an evolution towards European technologic autonomy, according to D’urso. “The loss of economic autonomy will impact political power. In other words, data and economic frailty will only further weaken Europe’s role at the global power table and open the door to a variety of potential flash points (military, cyber, industrial, social and so on). “Europe should be proud of its model, which re-injects tax revenues into a fair and respectful social and cultural framework. The GDPR policy is clearly at the heart of a European digital mindset.” Luc went further and suggested that data regulation, including management, protection and storage, is central to the upcoming French presidential election and the current French Presidency of the Council of the European Union. “The French Presidency of the Council of the EU will clearly place data protection into the spotlight of political debates. It is not about protectionism, but Europe must safeguard its data against foreign competition to enhance its autonomy and build a prosperous future.
Edge strategies that depend on one-off “snowflake” patterns for their success will cause long-term headaches. This is another area where experience with hybrid cloud architecture will likely benefit edge thinking: If you already understand the importance of automation and repeatability to, say, running hundreds of containers in production, then you’ll see a similar value in terms of edge computing. “Follow a standardized architecture and avoid fragmentation – the nightmare of managing hundreds of different types of systems,” advises Shahed Mazumder, global director, telecom solutions at Aerospike. “Consistency and predictability will be key in edge deployments, just like they are key in cloud-based deployments.” Indeed, this is an area where the cloud-edge relationship deepens. Some of the same approaches that make hybrid cloud both beneficial and practical will carry forward to the edge, for example. In general, if you’ve already been solving some of the complexity involved in hybrid cloud or multi-cloud environments, then you’re on the right path.
At its core, a scam is a situation in which the customer has been duped into initiating a fraudulent transaction that they believe to be authentic. Applying traditional controls for verifying or authenticating the activity may therefore fail. But the underlying ability to detect the anomaly remains critical. "Instead of validating the transaction or the individual, we are going to have to place more importance on helping the customer understand that what they believe to be legitimate is actually a lie," Mitchell of Omega FinCrime says. He says fraud operations teams will need to become more customer-centric, education-focused and careful in their interactions. Mitigating a scam apocalypse will require mobilization across the market, which includes financial institutions, solution providers, payment networks, regulators, telecom carriers, social media companies and law enforcement agencies. In the short term, investment priorities must expand beyond identity controls to include orchestration controls and decision support systems that allow financial institutions to see the interaction more holistically, Fooshee says.
Imagine a simple microservice with a producer and a consumer. When writing tests in the consumer project, you have to write mocks or stubs that model the behavior of the producer project. Conversely, when you write tests in the producer project, you have to write mocks or stubs that model the behavior of the consumer project. As such, multiple sets of related, redundant code have to be maintained in parallel in disconnected projects. ... “Mock” gets used in ways online that is somewhat generic, meaning any fake object used for testing, and this can get confusing when differentiating “mocks” from “stubs”. However, specifically, a “mock” is an object that tests for behavior by registering expected method calls before a test run. In contrast, a “stub” is a testable version of the object with callable methods that return pre-set values. Thus, a mock checks to see if the object being tested makes an expected sequence of calls to the object being mocked, and throws an error if the behavior deviates (that is, makes any unexpected calls). A stub does not do any testing itself, per se, but instead will return canned responses to pre-determined methods to allow tests to run.
Fortunately, AI tools and platforms have evolved to the point in which more governable, assembly-line approaches to development are possible, most of which are being harnessed under the still-evolving MLOps model. MLOps is already helping to cut the development cycle for AI projects from months, and sometimes years, down to as little as two weeks. Using standardized components and other reusable assets, organizations are able to create consistently reliable products with all the embedded security and governance policies needed to scale them up quickly and easily. Full scalability will not happen overnight, of course. Accenture’s Michael Lyman, North American lead for strategy and consulting, says there are three phases of AI implementation. ... To accelerate this process, he recommends a series of steps, such as starting out with the best use cases and then drafting a playbook to help guide managers through the training and development process. From there, you’ll need to hone your institutional skills around key functions like data and security analysis, process automation and the like.
In the world of cybersecurity, the most frequently asked question focuses on “who” is behind a particular attack or intrusion – and may also delve into the “why”. We want to know whom the threat actor or threat agent is, whether it is a nation state, organized crime, an insider, or some organization to which we can ascribe blame for what occurred and for the damage inflicted. Those less familiar with cyberattacks may often ask, “Why did they hack me?” As someone who has been responsible for managing information risk and security in the enterprise for 20-plus years, I can assure you that I have no real influence over threat actors and threat agents – the “threat” part of the above equation. These questions are rarely helpful, providing only psychological comfort, like a blanket for an anxious child, and quite often distract us from asking the one question that can really make a difference: “HOW did this happen?” But even those who asked HOW – have answered with simple vulnerabilities – we had an unpatched system, we lacked MFA, or the user clicked on a link.
Data analysts are one of the data consumers. A data analyst answers questions about the present such as: what is going on now? What are the causes? Can you show me XYZ? What should we do to avoid/achieve ABC? What is the trend in the past 3 years? Is our product doing well? ... Data scientists are another data consumer. Instead of answering questions about the present, they try to find patterns in the data and answer the questions about the future, i.e prediction. This technique has actually existed for a long time. You must have heard of it, it’s called statistics. Machine learning and deep learning are the 2 most popular ways to utilise the power of computers to find patterns in data. ... How do data analysts and scientists get the data? How does the data come from user behaviour to the database? How do we make sure the data is accountable? The answer is data engineers. Data consumers cannot perform their work without having data engineers set up the whole structure. They build data pipelines to ingest data from users’ devices to the cloud then to the database.
Another way machine unlearning could deliver value for both individuals and organizations is the removal of biased data points that are identified after model training. Despite laws that prohibit the use of sensitive data in decision-making algorithms, there is a multitude of ways bias can find its way in through the back door, leading to unfair outcomes for minority groups and individuals. There are also similar risks in other industries, such as healthcare. When a decision can mean the difference between life-changing and, in some cases, life-saving outcomes, algorithmic fairness becomes a social responsibility and often algorithms may be unfair due to the data they are being trained on. For this reason, financial inclusion is an area that is rightly a key focus for financial institutions, and not just for the sake of social responsibility. Challengers and fintechs continue to innovate solutions that are making financial services more accessible. From a model monitoring perspective, machine unlearning could also safeguard against model degradation.
Quote for the day:
"Good leaders make people feel that they're at the very heart of things, not at the periphery." -- Warren G. Bennis