Building Resilient and Secure FinTech Infrastructure: Strategies for DDoS Mitigation and Cybersecurity
Companies may have to pay anything from $1 million to over $5 million for every
hour of downtime, not to mention any further fines, fees, or penalties under the
law. It is in addition to higher DDoS security investments made after the fact
and higher cyber insurance premiums. As a result, DDoS victims may have to pay
for ransomware. However, this isn’t a fix. It does not ensure that a DDoS
assault won’t occur again. FinTech businesses need to be proactive if they want
total DDoS resilience. Regardless of the security services they use,
organizations are extremely susceptible to denial-of-service (DDoS) assaults.
The only way to withstand such attacks is to implement non-disruptive DDoS
testing and obtain uninterrupted and comprehensive insight into the DDoS
security posture. Continuous DDoS testing on live settings is necessary for
FinTech firms and their DDoS protection vendors to identify vulnerabilities,
prioritize remediation, and ensure that the solutions are applied appropriately.
Staying ahead of the threat curve means taking a preventive rather than a
reactive approach to safeguarding online services against DDoS attacks.
Data Governance Act: Understanding the Cross-Sectoral Instrument
The Data Governance Act sets out guidelines for the utilization within the EU
for the data held by the public sector entities. This data is secured and
protected for reasons such as commercial confidentiality, third-party
intellectual property rights and the protection of personal data. While the Act
does not explicitly detail the circumstances under which it is applied to the
foreign organizations, there are several provisions which imply its
extraterritorial implications. Any non-EU entity which provides services within
the European Union and qualifies as a data altruism company or intermediary must
appoint a legal representative in every member state it is operating in. ... The
Act complements the Open Data Directive by addressing the re-use of the secured
data which is not under the functions of the latter. It establishes several
secured walls for the re-utilization of such data held by the public sector
bodies including the governmental and other public sector entities. The Act does
not obligate the public sector bodies to permit the reuse of data but sets
conditions for any such authorization.
Six Data Quality Dimensions to Get Your Data AI-Ready
Compliance: The degree to which data is in accordance with laws, regulations, or
standards. How is the use of your data now changing? Do you need to uphold your
data to higher standards and different requirements in these new use cases?
Consider also: A true disruptor like GenAI may result in the need for new
policy, and therefore, new regulation. Can you anticipate future regulation
around AI usage given your industry and data? ... Accessibility: The ease with
which data can be consulted or retrieved. Scalability and repeatability require
consistent accessibility. Is your data reliably accessible by the right people
and technologies? How accessible must it be? If your data was temporarily
inaccessible, how damaging would that be? What is the acceptable threshold that
would ensure your project will succeed? Access Security: The degree to which
access to datasets is restricted. Consider the privileges and permissions to
your data and the implications. Are you building an AI tool in-house or are you
using a service? Which of your company’s data are you willing to provide third
party access to? Ensure that you are not sharing data that you cannot or should
not share.
Want to drive more secure GenAI? Try automating your red teaming
When red-teaming GenAI, manual probing is a time-intensive but necessary part of
identifying potential security blind spots. However, automation can help scale
your GenAI red teaming efforts by automating routine tasks and identifying
potentially risky areas that require more attention. At Microsoft, we released
the Python Risk Identification Tool for generative AI (PyRIT)—an open-access
framework designed to help security researchers and ML engineers assess the
robustness of their LLM endpoints against different harm categories such as
fabrication/ungrounded content like hallucinations, misuse issues like machine
bias, and prohibited content such as harassment. PyRIT is battle-tested by the
Microsoft AI Red Team. It started off as a set of one-off scripts as we began
red teaming GenAI systems in 2022, and we’ve continued to evolve the library
ever since. Today, PyRIT acts as an efficiency gain for the Microsoft AI Red
Team—shining a light on risk hot spots so that security professionals can then
explore them. This allows the security professional to retain control of the AI
red team strategy and execution.
A Novel AI Approach to Enhance Language Models: Multi-Token Prediction
The researchers behind this study propose a new technique called multi-token
prediction. Instead of predicting one token (word) at a time, this method trains
the model to predict multiple future tokens simultaneously. Imagine it like
this: While learning a language, instead of guessing one word at a time, you’re
challenged to predict entire phrases or even sentences. Sounds intriguing,
right? So, how does this multi-token prediction work? The researchers designed a
model architecture with a shared trunk that produces a latent representation of
the input context. This shared trunk is then connected to multiple independent
output heads, each responsible for predicting one of the future tokens. For
example, if the model is set to predict four future tokens, it will have four
output heads working in parallel. During training, the model is fed a text
corpus, and at each position, it is tasked with predicting the next n tokens
simultaneously. This approach encourages the model to learn longer-term patterns
and dependencies in the data, potentially leading to better performance,
especially for tasks that require understanding the broader context.
Uncomplicating the complex: How Spanner simplifies microservices-based architectures
Sharding is a powerful tool for database scalability. When implemented
correctly, it can enable applications to handle a much larger volume of read and
write transactions. However, sharding does not come without its challenges and
brings its own set of complexities that need careful navigation. ... Over time,
database complexity can grow along with increased traffic, adding further toil
to operations. For large systems, a combination of sharding along with attached
scale-out read replicas might be required to help ensure cost-effective
scalability and performance. This combined dual-strategy approach, while
effective in handling increasing traffic, significantly ramps up the complexity
of the system's architecture. The above illustration captures the need to add
scalability and availability to a transactional relational database powering a
service. ... We want to emphasize that we’re not arguing that Spanner is only a
good fit for microservices. All the things that make Spanner a great fit for
microservices also make it great for monolithic applications.
VPNs aren't invincible—5 things a VPN can't protect you from
A VPN can deter a hacker from trying to intercept your internet traffic, but it
cannot prevent you from landing on a scam website yourself or sharing your
personal details with someone on the web. Also, thanks to AI-powered tools,
attackers can craft increasingly convincing messages at high speed. This means
phishing attacks will keep happening in the future. The good news is that,
despite how well-made the messages are, you can always spot a scam. As a rule of
thumb, if something is too good to be true it generally is—so, beware of grand
promises. ... While a VPN keeps you more anonymous online, preventing some forms
of tracking, it only works at a network level. Tracking cookies, though, are
stored directly on your web browser. Hence, VPNs aren't much of a help against
such trackers. To mitigate the risks, I recommend clearing the internet cookies
on your devices on a regular basis. ... As we have seen, VPNs are not a magic
wand that'll magic away cyber threats and danger. Nonetheless, this software
still protects you from a great deal of risks and strongly enhances your digital
posture—so, all in all, VPNs are still vital pieces of security equipment.
Top Digital Transformation Themes and EA Strategies
Enterprise Architects (EAs) face significant challenges in engaging business
stakeholders. One of the main difficulties is shifting the perception of the EA
platform from a tool imposed by the EA team to a collaborative instrument that
benefits the entire business. EA teams also have a tendency to focus inwardly,
leaning on technical architectural terminology and objectives that other
business units may not understand or resonate with. ... Architects often
struggle to communicate the ROI of Enterprise Architecture or link architectural
initiatives and value directly to the business's overarching goals and metrics.
A common hurdle is the traditional IT-centric approach of EA, which may not
align with the dynamic needs of the business. ... The segregation of data into
siloes hampers transparency and makes it difficult to achieve a comprehensive
overview of the organization’s data landscape. So when architects try to bring
all this information together, the complexity often results in bottlenecks as
they struggle to manage and govern IT effectively. This severely limits the
potential for insights and efficiency across the organization.
How To Build a Scalable Platform Architecture for Real-Time Data
Many platforms enable autoscaling, like adjusting the number of running
instances based on CPU usage, but the level of automation varies. Some platforms
offer this feature inherently, while others require manual configuration, like
setting the maximum number of parallel tasks or workers for each job. During
deployment, the control plane provides a default setting based on anticipated
demand but continues to closely monitor metrics. It then scales up the number of
workers, tasks or instances, allocating additional resources to the topic as
required. ... Enterprises prioritize high availability, disaster recovery and
resilience to maintain ongoing operations during outages. Most data-streaming
platforms already have robust guardrails and deployment strategies built in,
primarily by extending their cluster across multiple partitions, data centers
and cloud-agnostic availability zones. However, it involves trade-offs like
increased latency, potential data duplication and higher costs. Here are some
recommendations when planning for high availability, disaster recovery and
resilience.
How will AI help democratise intelligence in algorithmic trading? Here are 5 ways
AI has played a significant role in making algorithmic trading more accessible
to the masses. The emergence of open AI sources like ChatGPT, and open source AI
models like Llama 3 has made AI technology available to anyone with an internet
connection at a mere $20 a month or so cost. This has led traders to gain access
to algorithms, creation of algorithms, that were previously only available to
large institutions. Additionally, many platforms have made it possible for
traders to automate their trades without requiring coding knowledge, thanks to
the contribution of AI in algo trading. ... Algorithms powered by market
research and AI can significantly reduce trading errors caused by emotions and
impulsive decisions. Traditional trading methods rely heavily on expertise,
intuition, and precision, whereas AI-powered algorithms eliminate the need for
these factors and enhance the accuracy, efficiency, and overall performance of
trades. These algorithms can be customised to fit various market situations,
whether it's a stable or volatile market.
Quote for the day:
"Courage doesn't mean you don't get
afraid. Courage means you don't let fear stop you." --
Bethany Hamilton
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