Quote for the day:
"Success is how high you bounce when you hit bottom." -- Gen. George Patton
Do Stablecoins Pave the Way for CBDCs? An Architect’s Perspective
The relationship between regulated stablecoins and CBDCs is complex. Rather than
being purely competitive, they may evolve to serve complementary roles in the
digital currency ecosystem. Regulated stablecoins excel at facilitating
cross-border transactions, supporting decentralised finance applications, and
serving as bridges between traditional and crypto financial systems. CBDCs,
meanwhile, are likely to focus on domestic retail payments, financial inclusion,
and maintaining monetary sovereignty. The regulated stablecoin market has
provided valuable lessons for CBDC implementation. Central banks have observed
how private stablecoins handle scalability challenges, privacy concerns, and
user experience issues. These insights are informing CBDC designs worldwide.
However, significant hurdles remain before CBDCs achieve widespread adoption.
Technical challenges around scalability, privacy, and security must be resolved.
Legal frameworks need updating to accommodate these new forms of money. Perhaps
most importantly, central banks must convince the sceptical public that CBDCs
will not become tools for surveillance or financial control.
Inside the war between genAI and the internet
One way to stop AI crawlers is via good old-fashioned robots.txt files, but as
noted, they can and often do ignore those. That’s prompted many to call for
penalties such as infringement lawsuits, for doing so. Another approach is to
use a Web Application Firewall (WAF), which can block unwanted traffic,
including AI crawlers, while allowing legitimate users to access a site. By
configuring the WAF to recognize and block specific AI bot signatures, websites
can theoretically protect their content. More advanced AI crawlers might evade
detection by mimicking legitimate traffic or using rotating IP addresses.
Protecting against this is time-consuming, forcing the frequent updating of
rules and IP reputation lists — another burden for the source sites. Rate
limiting is also used to prevent excessive data retrieval by AI bots. This
involves setting limits on the number of requests a single IP can make within a
certain timeframe, which helps reduce server load and data misuse risks.
Advanced bot management solutions are becoming more popular, too. These tools
use machine learning and behavioral analysis to identify and block unwanted AI
bots, offering more comprehensive protection than traditional methods.
How AI enhances security in international transactions
Rather than working with pre-set and heuristic rules, AI learns from transaction
patterns in real time. It doesn’t just flag transactions that exceed a certain
limit—it contextualises behaviour. ... If the transaction is genuinely out
of place, AI doesn’t immediately block it but escalates it for real-time review.
This ability to detect anomalies with context is what makes AI so much more
effective than rigid compliance rules. ... One of the biggest pain points in
compliance today is false positives, transactions wrongly flagged as suspicious.
Imagine a business that expands into a new market and suddenly sees a surge in
inbound transactions. Without AI, this might result in an account freeze. But
even AI-powered systems aren’t perfect. A name match in a sanctions list, for
instance, doesn’t necessarily mean the customer is a fraudster. If John Doe from
Mumbai is mistakenly flagged as Jon Doe from New York, who was implicated in a
financial crime, a manual review is still necessary. ... AI isn’t here to
replace compliance teams, it’s here to empower them. Instead of manually
reviewing thousands of transactions, compliance officers can focus on high-risk
cases while AI handles routine screening. What does the future look like?
Faster, real-time transaction approvals – AI will further reduce manual
interventions, making cross-border payments almost instantaneous.
DiRMA: Measuring How Your Organization Manages Chaos
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DiRT is a structured approach to stress-testing systems by intentionally
triggering controlled failures. Originally pioneered in large-scale technology
infrastructures, DiRT helps organizations proactively identify weaknesses and
refine their recovery strategies. Unlike traditional disaster recovery methods,
which rely on theoretical scenarios, DiRT forces teams to confront real
operational disruptions in a controlled manner, ensuring that failure responses
are both effective and repeatable. The methodology consists of performing a
coordinated and organized set of events, in which a group of engineers plan and
execute real and fictitious outages for a defined period to test the effective
response of the involved teams ... DiRMA is inspired by the program DiRT,
created in 2006 by Google to inject failures in critical systems, business
processes and people dynamics to expose reliability risks and provide preemptive
mitigations. Since some organizations have already started their journey toward
the creation of environments for DiRT, in which they can launch failures,
determine their level of resilience and test their incident response processes,
it is essential to have frameworks, like CE Maturity Assessments, to evaluate
the effectiveness, in this case, of a program like DiRT.
The RACI matrix: Your blueprint for project success
The golden rule of a RACI matrix is clarity of accountability. Because of this,
as mentioned previously, only one person can be accountable for a given project.
In many projects, the concept of responsibility and accountability can get
conflated or confused, especially when those responsible for the project’s
completion are empowered with broad decision-making capabilities. The chief
difference between R (responsible) and A (accountable) roles is that, while
those deemed responsible may be given latitude for decision-making when
completing the work involved in a task or project, only one person truly owns
and signs off on the work. ... RASCI is another type of responsibility
assignment matrix used in project management. It retains the four core roles of
RACI — Responsible, Accountable, Consulted, and Informed — but adds a fifth:
Supportive. The Supportive role in a RASCI chart is responsible for providing
assistance to those in the Responsible role. This may involve providing
additional resources, expertise, or advice to help the Responsible party
complete a particular task. Organizations that choose RASCI often do so to
ensure that personnel who may not have direct responsibility or accountability
but are nevertheless vital to the success of an activity or project are
considered a notable facet (and cost) of the project.
How to create an effective crisis communication plan
Planning crisis communication involves many practical aspects. These include,
for example, identifying the room in which live crisis management meetings can
take place and how online meetings will be conducted. In the event of a cyber
crisis, it must always be taken into account that communication tools such as
email, chat, landline, or IP telephony may not be available. It must also be
expected that the IT network will be inaccessible or will have to be shut down
for security reasons. Therefore, all prepared documents and contact lists of the
crisis team must be accessible even without access to the internal IT network.
... Crucial to effective external communications is that the media and social
network users receive information from a single source. Therefore, it must be
clarified that only designated corporate communications employees with
experience in public relations will provide statements to the media. All
departments must be informed of their media contact details. Press relations
during a crisis are generally conducted in multiple stages. Immediately upon the
outbreak of a crisis, a prepared statement must be made available and issued on
request. This statement may not contain details about the incident itself, but
must express a willingness to engage in open communication.
Tapping into the Unstructured Data Goldmine for Enterprise in 2025
With so much structured data on hand, companies may believe unstructured data
doesn’t add value, which couldn’t be farther from the truth. In fact,
unstructured data can provide deeper insights and put companies ahead of the
competition. However, before that happens, organizations must get a handle on
all of the data they have on hand. While the majority of unstructured data is
digital, some businesses have a large number of paper records that haven’t yet
been digitized. By using a combination of software and document scanners, hard
copies can be scanned and integrated with unstructured data. This may seem like
too much of an investment from a time and resource perspective, and a heavy lift
for humans alone; however, AI can fundamentally change how companies leverage
unstructured data, enabling organizations to extract valuable insights and drive
decision-making through human/machine collaboration. ... There’s no doubt that
effectively managing unstructured data is critical to a successful and holistic
data management program, but managing it can be complex, overwhelming,
resource-intensive and difficult to analyze because it doesn’t fit neatly into
traditional databases. Unlike structured data, which can easily be turned into
business intelligence, unstructured data often requires significant processing
before it can provide actionable insights.
Advances in Data Lakehouses
Recent advancements in data lakehouse architecture have significantly enhanced
data management and quality through innovations like Delta Lake, ACID
transactions, and metadata management. Delta Lake acts as a storage layer on top
of existing cloud storage systems, introducing robust features such as ACID
transactions that ensure data integrity and reliability. This enables consistent
read and write operations, reducing the risk of data corruption and making it
easier for organizations to maintain reliable datasets. Additionally, Delta Lake
supports schema enforcement and evolution, allowing for more flexible data
handling while maintaining structural integrity. Metadata management in a data
lakehouse context provides a comprehensive way to manage data assets, enabling
efficient data discovery and governance. ... In the rapidly evolving landscape
of data management, improving query performance and enhancing SQL compatibility
are crucial for modern data stacks, especially within the framework of data
lakehouses. Data lakehouses combine the best of data lakes and data warehouses,
providing both the scalability of lakes for raw data storage and the structured,
efficient querying capabilities of warehouses. A primary focus in this area is
optimizing query engines to handle diverse workloads efficiently.
Self-Healing Data Pipelines: The Next Big Thing in Data Engineering?
The idea of a self-healing pipeline is simple: When errors occur during data
processing, the pipeline should automatically detect, analyze, and correct them
without human intervention. Traditionally, fixing these issues requires manual
intervention, which is time-consuming and prone to errors. There are several
ways to idealize this, but using AI agents is the best method and a futuristic
approach for data engineers to self-heal failed pipelines and auto-correct them
dynamically. In this article, I will show a basic implementation of how to use
LLMs like the GPT-4/DeepSeek R1 model to self-heal data pipelines by using LLM’s
recommendation on failed records and applying the fix through the pipeline while
it is still running. The provided solution can be scaled to large data pipelines
and extended to more functionalities by using the proposed method. ... To ensure
resilience, we implement a retry mechanism using tenacity. The function sends
error details to GPT and retrieves suggested fixes. In our case, the 'functions'
list was created and passed to the JSON payload using the ChatCompletion
Request. Note that the 'functions' list is the list of all functions available
to fix the known or possible issues using the Python functions we have created
in our pipeline code.
Android financial threats: What businesses need to know to protect themselves and their customers
Research has revealed an alarming trend around Android-targeted financial
threats. Attackers are leveraging Progressive Web Apps (PWAs) and Web Android
Package Kits (WebAPKs) to create malicious applications that can bypass
traditional app store vetting processes and security warnings. The mechanics of
these attacks are sophisticated yet deceptively simple. Victims are typically
lured in through phishing campaigns that exploit various communication channels,
including SMS, automated calls, and social media advertisements. ...
Educating customers is a vital step. Businesses can empower customers by
highlighting their own security efforts, like two-factor authentication and
secure transactions. By making security part of their brand identity and
providing supportive resources, small and mid-size businesses can create a safe,
confident experience for their customers. Strengthening internal security
measures is equally important though. Small businesses should consider
implementing mobile threat detection solutions capable of identifying and
neutralizing malicious PWAs and WebAPKs. Additional measures include
collaborating with financial partners, sharing intelligence on emerging threats
and developing coordinated incident response plans to address attacks quickly
and effectively.
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