DevSecOps: Building security into each stage of development
While it is important and becoming invaluable, it’s difficult to know how well
open-source code has been maintained, Faus noted. A developer might incorporate
third-party code and inadvertently introduce a vulnerability. DevSecOps allows
security teams to flag that vulnerability and work with the development team to
identify whether the code should be written differently or if the vulnerability
is even dangerous. Ultimately, all parties can assure that they did everything
they could to produce the most secure code possible. In both DevOps and
DevSecOps, “the two primary principles are collaboration and transparency,” Faus
said. Another core tenet is automation, which creates repeatability and reuse.
If a developer knows how to resolve a specific vulnerability, they can reuse it
across every other project with that same vulnerability. ... One of the biggest
challenges in implementing security throughout the development cycle is the
legacy mindset in how security is treated, Faus pointed out. Organizations must
be willing to embrace cultural change and be open, transparent, and
collaborative about fixing security issues. Another challenge lies in building
in the right type of automation. “One of the first things is to make security a
requirement for every new project,” Faus said.
Curb Your Hallucination: Open Source Vector Search for AI
Vector search—especially implementing a RAG approach utilizing vector data
stores—is a stark alternative. Instead of relying on a traditional search engine
approach, vector search uses the numerical embeddings of vectors to resolve
queries. Therefore, searches examine a limited data set of more contextually
relevant data. The results include improved performance, earned by efficiently
utilizing massive data sets, and greatly decreased risk of AI hallucinations. At
the same time, the more accurate answers that AI applications provide when
backed by vector search enhance the outcomes and value delivered by those
solutions. Combining both vector and traditional search methods into hybrid
queries will give you the best of both worlds. Hybrid search ensures you cover
all semantically related context, and traditional search can provide the
specificity required for critical components ... Several open source
technologies offer an easy on-ramp to building vector search capabilities and a
path free from proprietary expenses, inflexibility, and vendor lock-in risks. To
offer specific examples, Apache Cassandra 5.0, PostgreSQL (with pgvector), and
OpenSearch are all open source data technologies that now offer enterprise-ready
vector search capabilities and underlying data infrastructure well-suited for AI
projects at scale.
Driving Serverless Productivity: More Responsibility for Developers
First, there are proactive controls, which prevent deployment of non-compliant
resources by instilling best practices from the get-go. Second, detective
controls, which identify violations that are already deployed, and then provide
remediation steps. It’s important to recognize these controls must not be
static. They need to evolve over time, just as your organization, processes, and
production environments evolve. Think of them as checks that place more
responsibility on developers to meet high standards, and also make it far easier
for them to do so. Going further, a key -- and often overlooked -- part of any
governance approach is its notification and supporting messaging system. As your
policies mature over time, it is vitally important to have a sense of lineage.
If we’re pushing for developers to take on more responsibility, and we’ve
established that the controls are constantly evolving and changing,
notifications cannot feel arbitrary or unsupported. Developers need to be able
to understand the source of the standard driving the control and the symptoms of
what they’re observing.
Mastering Observability: Unlocking Customer Insights
If we do something and the behaviour of our users changes in a negative way, if
they start doing things slower, less efficiently, then we're not delivering
value to the market. We're actually damaging the value we're delivering to the
market. We're disrupting our users' flows. So a really good way to think about
whether we are creating value or not is how is the behavior of our users, of our
stakeholders or our customers changing as a result of us shipping things out?
And this kind of behavior change is interesting because it is a measurement to
whether we are solving the problem, not whether we're delivering a solution. And
from that perspective, I can then offer five different solutions for the same
behavior change. I can say, "Well, if that's the behavior change we want to
create, this thing you proposed is going to cost five men millennia to make, but
I can do it with a shell script and it's going to be done tomorrow. Or we can do
it with an Excel export or we can do it with a PDF or we can do it through a
mobile website not building a completely new app". And all of these things can
address the same behavior change.
AI-generated code is causing major security headaches and slowing down development processes
The main priorities for DevSecOps in terms of security testing were the
sensitivity of the information being handled, industry best practice, and easing
the complexity of testing configuration through automation, all cited by around
a third. Most survey respondents (85%) said they had at least some measures in
place to address the challenges posed by AI-generated code, such as potential
IP, copyright, and license issues that an AI tool may introduce into proprietary
software. However, fewer than a quarter said they were ‘very confident' in their
policies and processes for testing this code. ... The big conflict here appears
to be security versus speed considerations, with around six-in-ten reporting
that security testing significantly slows development. Half of respondents also
said that most projects are still being added manually. Another major hurdle for
teams is the dizzying number of security tools in use, the study noted. More
than eight-in-ten organizations said they're using between six and 20 different
security testing tools. This growing array of tools makes it harder to integrate
and correlate results across platforms and pipelines, respondents noted, and is
making it harder to distinguish between genuine issues and false positives.
How digital twins are revolutionising real-time decision making across industries
Despite the promise of digital twins, Bhonsle acknowledges that there are
challenges to adoption. “Creating and maintaining a digital twin requires
substantial investments in infrastructure, including sensors, IoT devices, and
AI capabilities,” he points out. Security is another concern, particularly in
industries like healthcare and energy, where compromised data streams could lead
to life-threatening consequences. However, Bhonsle emphasises that the rewards
far outweigh the risks. “As digital twin technology matures, it will become more
accessible, even to smaller organisations, offering them a competitive edge
through optimised operations and data-driven decisions.” ... Digital twins are
transforming how businesses operate by providing real-time insights that drive
smarter decisions. From manufacturing floors to operating rooms, and from energy
grids to smart cities, this technology is reshaping industries in unprecedented
ways. As Bhonsle aptly puts it, “The rise of digital twins signals a new era of
efficiency and agility—an era where decisions are no longer based on assumptions
but driven by data in real time.” As organisations embrace this evolving
technology, they unlock new opportunities to optimise performance and stay ahead
in a fast-changing world.
AI and tech in banking: Half the industry lags behind
Gupta emphasised that a superficial approach to digitalisation—what he called
“putting lipstick on a pig”—is common in many institutions. These banks often
adopt digital tools without rethinking the processes behind them, resulting in
inefficiencies and missed opportunities for transformation. In addition, the
culture of risk aversion in many financial institutions makes them slow to
experiment with new technologies. According to a Deloitte survey, 62% of banking
executives cited corporate culture as the biggest barrier to successful digital
transformation. A fear of regulatory hurdles and data privacy issues also
compounds this reluctance to fully embrace AI. ... The rise of fintech companies
is also reshaping the financial landscape. Digital-first challengers like
Revolut and Monzo are making waves by offering streamlined, customer-centric
services that appeal to tech-savvy users. These companies, unencumbered by
legacy systems, have been able to rapidly adopt AI, providing highly
personalised products and services through their digital platforms. The UK
fintech sector alone saw record investment in 2021, with $11.6 billion pouring
into the industry, according to Innovate Finance. This influx of capital has
enabled fintech firms to invest in AI technologies, providing stiff competition
to traditional banks that are slower to adapt.
The Era Of Core Banking Transformation Trade-offs Is Finally Over
There must be a better way than forcing banks to choose their compromises.
Banking today needs a next-generation solution that blends the best of
configuration neo cores – speed to market, lower cost, derisked migration – and
combines it with the benefits of framework neo cores – full customization of
products and even the core, with massive scale built in as standard. If banks
and financial services aren’t forced to compromise because of their choice of
cloud-native core solution, they can accelerate their innovation. Our research
reveals that, while AI remains front of mind for many IT decision makers in
banking and financial services, only one in three (32%) have so far integrated
AI into their core solution. This is concerning. According to (McKinsey),
banking is one of the top four sectors set to capitalize on that market
opportunity – but that forecast will remain a pipe dream if banks can’t
integrate AI efficiently, securely and at massive scale. ... One thing is
certain, whether configuration or framework, neo cores are not the final
destination for banking. They have been a helpful stepping stone over the last
decade to cloud-native technology, but banks and financial services now need a
next-generation core technology that doesn’t demand so many
compromises.
10 Risks of IoT Data Management
IoT data management faces significant security risks due to the large attack
surface created by interconnected devices. Each device presents a potential
entry point for cyberattacks, including data breaches and malware injections.
Attackers may exploit vulnerabilities in device firmware, weak authentication
methods, or unsecured network protocols. To mitigate these risks, implementing
end-to-end encryption, device authentication, and secure communication channels
is essential. ... IoT devices often collect sensitive personal information,
which raises concerns about user privacy. The lack of transparency in how data
is collected, processed, and shared can erode user trust, especially when data
is shared with third parties without explicit consent. Addressing privacy
concerns requires the anonymization and pseudonymization of personal data. ...
The massive influx of data generated by IoT devices can overwhelm traditional
storage systems, leading to data overload. Managing this data efficiently is a
challenge, as continuous data generation requires significant storage capacity
and processing power. To solve this, organizations can adopt edge computing,
which processes data closer to the source, reducing the need for centralized
storage.
Managing bank IT spending: Five questions for tech leaders
The demand for development resources and the need to manage tech debt are only
expected to increase. Tech talent has never been cheap, and inflation is pushing
up salaries. Cybersecurity threats are also becoming more urgent, demanding
greater funds to address them. And figuring out how to integrate generative AI
takes time, personnel, and money. Despite these competing priorities and
challenges, bank IT leaders have an opportunity to make their mark on their
organizations and position themselves as central to their success—if they can
address some key problems. ... In our experience, IT leaders should never
underestimate the importance of controlling and reducing tech debt whenever
possible. Actions to correct course could include conducting frequent
assessments to determine which areas are accumulating tech debt and developing
plans to reduce it as much as possible. More than many other industries, banking
is a hotbed of new app development. Leaders who address these key questions can
ensure they are directing their talent and resources to game-changing app
development that directly contributes to their bank’s bottom line.
Quote for the day:
“It's failure that gives you the proper
perspective on success.” -- Ellen DeGeneres
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