A double-edged sword: GenAI vs GenAI
Every technology indeed presents new avenues for vulnerabilities, and the key lies in maintaining strict discipline in identifying and addressing these vulnerabilities. This calls for the strict application of IT ethos in organisational setups to ensure no misuse of technologies, especially intelligent ones. “It is crucial to continuously test your APIs and applications, relentlessly seeking out any potential vulnerabilities and ensuring they are addressed promptly. This proactive approach is vital in safeguarding your platform against potential threats,” says Sunil Sapra, Co-founder & Chief Growth Officer, Eventus Security. The Government of India has proactively addressed the grave importance of cybersecurity and recently rolled out the much-awaited Digital Personal Data Protection Act 2023. The Act though takes into consideration data protection and data privacy laying emphasis on the ‘consent of the owner’, but it does not draw the spotlight on GenAI that can make or break the existing cyber fortifications. Hence, there is a dire need for strong regulations and control measures guarding the application of GenAI models.
There's more to cloud architecture than GPUs
GPUs require a host chip to orchestrate operations. Although this simplifies the complexity and capability of modern GPU architectures, it’s also less efficient than it could be. GPUs operate in conjunction with CPUs (the host chip), which offload specific tasks to GPUs. Also, these host chips manage the overall operation of software programs. Adding to this question of efficiency is the necessity for inter-process communications; challenges with disassembling models, processing them in parts, and then reassembling the outputs for comprehensive analysis or inference; and the complexities inherent in using GPUs for deep learning and AI. This segmentation and reintegration process is part of distributing computing tasks to optimize performance, but it comes with its own efficiency questions. Software libraries and frameworks designed to abstract and manage these operations are required. Technologies like Nvidia’s CUDA (Compute Unified Device Architecture) provide the programming model and toolkit needed to develop software that can harness GPU acceleration capabilities.
How to Evaluate the Best Data Observability Tools
Some key areas to evaluate for enterprise readiness include:Security– Do they
have SOC II certification? Robust role based access controls? Architecture– Do
they have multiple deployment options for the level of control over the
connection? How does it impact data warehouse/lakehouse performance? Usability–
This can be subjective and superficial during a committee POC so it’s important
to balance this with the perspective from actual users. Otherwise you might
over-prioritize how pretty an alert appears versus aspects that will save you
time such as ability to bulk update incidents or being able to deploy
monitors-as-code. Scalability– This is important for small organizations and
essential for larger ones. We all know the nature of data and data-driven
organizations lends itself to fast, and at times unexpected growth. What are the
largest deployments? Has this organization proven its ability to grow alongside
its customer base? Other key features here include things like ability to
support domains, reporting, change logging, and more. These typically aren’t
flashy features so many vendors don’t prioritize them.
CISA releases draft rule for cyber incident reporting
According to the proposed rules, CISA plans to use the data it receives to carry
out trend and threat analysis, incident response and mitigation, and to inform
future strategies to improve resilience. While the rule is not expected to be
finalized until 18 months from now or potentially later next year, comments are
due 60 days after the proposal is officially published on April 4. One can be
sure that the 16 different critical infrastructure sectors and their armies of
lawyers will have much to say. The 447-page NOPR details a dizzying array of
nuances for specific sectors and cyber incidents. ... The list of exceptions to
the cyber incidents that critical infrastructure operators will need to report
is around twice as long as the conditions that require reporting an incident,
and the final shape of the rule may change as CISA considers comments from
industry. The companies affected by the proposed rules include all critical
infrastructure entities that exceed the federal government’s threshold for what
is a small business. The rules provide a series of different criteria for
whether other critical infrastructure sectors will be required to report
incidents.
Digital transformation’s fundamental change management mistake
the bigger challenge is often downstream and occurs when digital trailblazers,
the people assigned to lead digital transformation initiatives, must work with
end-users on process changes and technology adoption. When devops teams release
changes to applications, dashboards, and other technology capabilities,
end-users experience a productivity dip before people effectively leverage new
capabilities. This dip delays when the business can start realizing the value
delivered. While there are a number of change management frameworks and
certifications, many treat change as separate disciplines from the product
management, agile, and devops methodologies CIOs use to plan and deliver digital
transformation initiatives. ... Reducing productivity dips and easing
end-user adoption then are practices that must fit the digital and
transformation operating model. Let’s consider three areas where CIOs and
digital trailblazers can inject change management into their digital
transformation initiatives in a way that brings greater effectiveness than if
change management were addressed as a separate add-on.
6 keys to navigating security and app development team tensions
Unfortunately, many organizations don’t take the proper steps, leading to the
development team viewing security teams as a “roadblock” — a hurdle to
overcome. Likewise, the security team’s animosity toward development teams
grows as they view developers as not “taking security seriously enough.” ...
When you have an AppSec team built just by security people who have never
worked in development, that situation will likely cause friction between the
two groups because they will probably always speak two languages. And neither
group understands the problems and challenges the other team faces. When you
have an AppSec team that includes prior developers, you will see a much
different relationship between the teams. ... Sometimes, there are
unreasonable requests because the security team asks for things that aren’t
actual issues to be fixed. This happens when they run an application
vulnerability scanner, and the scanner reports a vulnerability that doesn’t
exist or expose an actual risk. The security team blindly passed that on to
developers to remedy.
Enhancing Business Security and Compliance with Service Mesh
When implementing a service mesh, there are several important factors you
should consider for secure and compliant deployment.You should carefully
evaluate the security features and capabilities of the chosen service mesh
framework. Look for strong authentication methods like mutual TLS and support
for role-based access control (RBAC) to ensure secure communication between
services. Establish clear policies and configurations for traffic management,
such as circuit breaking and request timeouts, to mitigate the risk of
cascading failures and improve overall system resilience. Thirdly,
consider the observability aspects of the service mesh. Ensure that metrics,
logging, and distributed tracing are properly configured to gain insights into
service mesh behavior and detect potential security incidents. For example,
leverage tools like Prometheus for metrics collection and Grafana for
visualization to monitor key security metrics such as error rates and latency.
Maintaining regular updates and patches for the service mesh framework is
important to address any security vulnerabilities promptly. You should stay
informed about the latest security advisories and best practices provided by
the service mesh community.
Who should be the head of generative AI — and what they should do
Some generative AI leaders might have a creative background; others could come
from tech. Gratton said background matters less than a willingness to
experiment. “You want somebody who’s got an experimental mindset, who sees
this as a learning opportunity and sees it as an organizational structuring
issue,” she said. “The innovation part is what’s really crucial.” ... The head
of AI could encourage use of the technology to help with managing employees,
Gratton said. This encompasses three key areas: Talent development -
Companies can use chatbots and other tools to recruit people and help them
manage their careers. Productivity - AI can be used to create
assessments, give feedback, manage collaboration, and provide skills training.
Change management - This includes both internal and external knowledge
management. “We have so much knowledge in our organizations … but we don’t
know how to find it,” Gratton said. “And it seems to me that this is an area
that we’re really focusing on in terms of generative AI.” ... Leaders should
remember that buy-in across all career stages and skill levels is essential.
Generative AI isn’t just the domain of youth.
Knowledge-Centered Design for Generative AI in Enterprise Solutions
The need for a new design pattern, specifically the Knowledge Centered Design
(KCD), arises from the evolution and complexity of AI and machine learning
technologies. As these technologies advance, they generate an increasing
volume of knowledge and insights. The traditional Human-Centered Design (HCD)
focuses on understanding users, their tasks, and environments. However, it may
not be fully equipped to handle the intricate dynamics of both human-generated
and AI-generated knowledge effectively. The proposed KCD extends HCD by
emphasizing the life cycle of knowledge – identifying, acquiring,
categorizing, extracting insights – and incorporating feedback loops for
continuous improvement. It ensures that both human-based and AI-generated
knowledge are effectively integrated into the design process to enhance user
experience and productivity. ... The knowledge life cycle process, feedback
loop process, and integral components of the KCD pattern, serve as starting
baselines that each enterprise can adapt and adjust according to their
specific business needs and institutional culture.
Creating a Data Monetization Strategy
Monetizing customer data involves implementing effective strategies and
adhering to best practices to maximize its value. One key approach is to
ensure
data privacy
and security, as customers are increasingly concerned about the usage of their
personal information. Companies must establish robust data protection
measures, comply with regulations such as GDPR or CCPA, and obtain explicit
consent for data collection and utilization. Another strategy is to leverage
advanced analytics techniques to derive valuable insights from customer data.
By employing ML algorithms, predictive modeling, and artificial intelligence,
businesses can uncover patterns, preferences, and trends. ... Blockchain
technology is revolutionizing how data is monetized by enhancing security and
trust in the digital ecosystem. Blockchain, a decentralized and immutable
ledger, provides a robust infrastructure for securely storing and transferring
data, making it an ideal solution for data monetization. Additionally, every
transaction recorded on the blockchain is encrypted and linked to previous
transactions through cryptographic hash functions, further safeguarding the
integrity of the data.
Quote for the day:
"It is during our darkest moments that
we must focus to see the light." -- Aristotle Onassis
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