7 Practices to Bolster Cloud Security and Keep Attackers at Bay
AI tools can facilitate quicker threat detection, investigation, and response. All healthy cloud security postures should utilize ML-based user and entity behavior analytics (UEBA) tools. Such tools effectively identify anomalous behavior across the network, while facilitating rapid investigation of potential threats and automating responses to mitigate and remediate attacks. Ideally, security professionals want to find vulnerabilities before an attack occurs, and such AI tools can help to do just that. ... When a threat occurs in the cloud, it can sometimes be difficult to assess the potential impact across a distributed or multitenant surface. By utilizing a centralized platform, security personnel have access to a response center that can automate workflows by orchestrating with different cloud applications, which in turn reduces the mean time to resolve (MTTR) incidents and threats. ... By correlating access and security logs from cloud applications, security personnel can identify attempts at data exfiltration from the cloud. As a quick example, if a SOC professional is investigating potential customer data exfiltration from a cloud-based CRM tool, he or she would want to correlate the logs of that CRM tool with the logs of other cloud applications, such as email or team communication tools.
6 AI-Related Security Trends to Watch in 2025
As more organizations work to embed AI capabilities into their software, expect to see DevSecOps, DataOps, and ModelOps — or the practice of managing and monitoring AI models in production — converge into a broader, all-encompassing xOps management approach, Holt says. The push to AI-enabled software is increasingly blurring the lines between traditional declarative apps that follow predefined rules to achieve specific outcomes, and LLMs and GenAI apps that dynamically generate responses based on patterns learned from training data sets, Holt says. ... The easy availability of a wide and rapidly growing range of GenAI tools has fueled unauthorized use of the technologies at many organizations and spawned a new set of challenges for already overburdened security teams. ... The easy availability of a wide and rapidly growing range of GenAI tools has fueled unauthorized use of the technologies at many organizations and spawned a new set of challenges for already overburdened security teams. ... "If unchecked, this raises serious questions and concerns about data loss prevention as well as compliance concerns as new regulations like the EU AI Act start to take effect," she says.
Working in Cyber Threat Intelligence (CTI)
“The analysis of an adversary’s intent, opportunity, and capability to do harm
is known as cyber threat intelligence.” It’s not just about finding some
IOCs and sending them to the SOC. It’s about providing context about adversary
activity for other security teams to help prioritize cyber defense efforts.
While there are more steps than this, in short we collect intrusion data and
analyze it, looking for correlations and trends to observed malicious activity.
With that analyzed activity and trends, we can provide actionable insights into
malicious activity to keep defenders focused only on the most relevant. ...
Aside from everything in the “What CTI Isn’t” section, the biggest challenge in
CTI is that it’s next to impossible to get decent intel requirements. “Just get
us intel” isn’t a thing. We need information to give relevant information. What
strategic initiatives, products, technologies, partnerships, etc. are of
particular interest to the leadership? What are all of your countries of
operation? What are considered the most critical assets? How would a threat
actor achieving their objectives impede the organization’s mission? It
unfortunately is an ongoing problem that many CTI analysts and CTI management
struggle with. This often leads to intel analysts winging it.
What’s Ahead in Generative AI in 2025?
In the coming year, prompt engineering will continue its rapid maturation into a
substantial body of proven practices for eliciting the correct output from LLMs
and other foundation models. Within generative AI development tool sets,
embedding libraries will become an essential component for developers to build
increasingly sophisticated similarity searches that span a diverse range of data
modalities. The recent TDWI survey on enterprise AI readiness shows that 28% of
organizations already use or are deploying vector databases to store vector
embeddings for use with AI models, while 32% plan to adopt those databases in
the next few years. In addition, generative AI developers in 2025 will have
access to a growing range of tools for no-code development of “agentic”
applications that provide autonomous LLM-driven copilot, chatbot, and other
functionality and that can be orchestrated over more complex process
environments. ... Developers will have access in 2025 to a growing range of
sophisticated models and data for building, training, and optimizing generative
AI applications—including both commercial and open-source models. The recent
TDWI survey on data and analytics trends showed that around 25% of enterprises
are experimenting with private or public generative AI models, while 17% are
building generative AI apps that use company data with pretrained models.
This Is The Phrase That Instantly Damages Your Leadership Integrity
There are few phrases that have the ability to instantly cause hesitation like
the phrase “to be honest with you.” Here are a few other honorable mentions
that cause the same damage for the same reasons. In all honesty… Frankly… To
tell you the truth… Truthfully or truthfully speaking… When you casually use a
statement like “to be honest with you,” in an effort to ensure that you’re
more likely to be believed, the exact opposite happens. Instead of trusting
you more, listeners trust you less. ... Without leadership integrity, you’d
have a very heavy lift trying to get people to believe in you, to listen to
you, to count on you and to give you the benefit of the doubt that leaders so
desperately need during times of uncertainty, ambiguity and crisis. This is
why you don’t want to damage your leadership integrity or cause people to
question your credibility by throwing out unthoughtful words or phrases that
could give them pause. ... Instead of saying something like “mistakes were
made,” which shows a complete lack of leadership integrity and sends the
signal that someone somewhere made a mistake but you take no ownership for it.
Go ahead and accept responsibility and show that you are accountable for the
mistake and for the resolution as well.
Generative AI is not going to build your engineering team for you
Generative AI is like a junior engineer in that you can’t roll their code off
into production. You are responsible for it—legally, ethically, and
practically. You still have to take the time to understand it, test it,
instrument it, retrofit it stylistically and thematically to fit the rest of
your code base, and ensure your teammates can understand and maintain it as
well. The analogy is a decent one, actually, but only if your code is
disposable and self-contained, i.e. not meant to be integrated into a larger
body of work, or to survive and be read or modified by others. And hey—there
are corners of the industry like this, where most of the code is write-only,
throwaway code. ... To state the supremely obvious: giving code review
feedback to a junior engineer is not like editing generated code. Your effort
is worth more when it is invested into someone else’s apprenticeship. It’s an
opportunity to pass on the lessons you’ve learned in your own career. Even
just the act of framing your feedback to explain and convey your message
forces you to think through the problem in a more rigorous way, and has a way
of helping you understand the material more deeply. And adding a junior
engineer to your team will immediately change team dynamics. It creates an
environment where asking questions is normalized and encouraged, where
teaching as well as learning is a constant.
Architectural Decision-Making: AI Tools as Consensus Builders
In an environment with lots of smart, quick-thinking people it can be a
challenge to ensure everyone is heard, especially when the primary mode of
interaction is videoconferencing. The online format (a Microsoft Teams group
chat) gave people time to contribute their thoughts over a period of days rather
than minutes. At various points in the online conversation, participants
extracted content from the online discussion board and fed it to a large
language model to compare ideas that were present in the dialogue, or to recast
the dialogue in a particular person’s voice. ... The benefits of using AI tools
are not cost free. It’s important to verify the results of an AI’s synthesis of
text because sometimes the AI misinterprets what was written. For example,
during our discussion of capabilities and domains, an AI tool interpreted some
of my text as stating that the boundaries of a domain are context dependent when
in fact, I was making the opposite argument – that a domain must have a
consistent definition that is valid across any contexts in which it
participates. Another consideration is the ethics of intellectual property
ownership and citation of participants’ contributions.
Perhaps the biggest challenge of IaC operations is drifts — a scenario where
runtime environments deviate from their IaC-defined states, creating a
festering issue that could have serious long-term implications. These
discrepancies undermine the consistency of cloud environments, leading to
potential issues with infrastructure reliability and maintainability and even
significant security and compliance risks. ... But having additional context
for drift, as important as it may be, is only one piece of a much bigger
puzzle. Managing large cloud fleets with codified resources introduces more
than just drift challenges, especially at scale. Current-gen IaC management
tools are effective at addressing resource management, but the demand for
greater visibility and control in enterprise-scale environments is introducing
new requirements and driving their inevitable evolution. ... The combination
of IaC management and CAM empowers teams to manage complexity with clarity and
control. As the end of the year approaches, it's 'prediction season' — so
here’s mine. Having spent the better part of the last decade building and
refining one of the more popular IaC management platforms, I see this as the
natural progression of our industry: combining IaC management, automation, and
governance with enhanced visibility into non-codified assets.
4 keys for writing cross-platform apps
One big problem with cross-platform compiling is how asymmetrical it can be.
If you’re a macOS user, it’s easy to set up and maintain Windows or Linux
virtual machines on the Mac. If you use Linux or Windows, it’s harder to
emulate macOS on those platforms. Not impossible, just more difficult—the
biggest reason being the legal issues, as macOS’s EULA does not allow it to be
used on non-Apple hardware. The easiest workaround is to simply buy a separate
Macintosh system and use that. Another option is to use tools like osxcross to
perform cross-compilation on a Linux, FreeBSD, or OpenBSD system. Another
common option, one most in line with modern software delivery methods, is to
use a system like GitHub Actions. The downside is paying for the use of
the service, but if you’re already invested in either platform, it’s often the
most economical and least messy approach. Plus, it keeps the burden of system
maintenance out of your hands. ... The way we write and deploy apps is always
in flux. Who would have anticipated the container revolution, for instance? Or
predicted the dominant language for machine learning and AI would be
Python? To that end, it’s always worth keeping an eye on the future, since
cross-platform deployment is fast becoming a must-have feature.
The Connected Revolution: How Integrated Intelligence is Reshaping Drug Development
CI and end-to-end quality are dismantling traditional silos and fostering a
seamless, data-driven ecosystem. The use of CI, potentially with data lakes as
a way of consolidating vast amounts of data from disparate sources, removes
silos that exist between independent systems sitting with siloed departments.
The movement of data, for example clinical data that is needed in regulatory
submissions, or safety data that is needed alongside regulatory data for
regulatory reports, brings a level of fluidity to data management and helps
companies optimize time and resources to generate product quality and safety
insights. ... For clinical trials, CI and end-to-end quality can significantly
enhance patient recruitment and retention. Advanced analytics can identify
suitable candidates more efficiently, while real-time monitoring through
connected devices can provide continuous data on patient responses and the
identification of potential adverse events. This improves the quality of data
collected, enhances patient safety and reduces trial time and cost. ... CI and
AI-driven regulatory intelligence, in the context of quality-controlled
procedures, can support the gathering of global submission requirements and
the creation of global submission content, which will then be subject to human
review as part of QC.
Quote for the day:
"A leader is best when people barely
know he exists, when his work is done, his aim fulfilled, they will say: we
did it ourselves." -- Laotzu
