Concerns over the security of electronic personal health information intensifies
When entities outside HIPAA’s purview experience breaches, the Federal Trade
Commission (FTC) Health Breach Notification Rule applies. However, this dual
system creates confusion among stakeholders, who must navigate overlapping
jurisdictions. The lack of a unified, comprehensive framework exacerbates the
problem, leaving patients uncertain about the security of their health data.
Another pressing concern is the cybersecurity of medical devices. Many modern
medical devices connect to networks or the internet, increasing their
susceptibility to cyberattacks. Hospitals often operate thousands of
interconnected devices, making it challenging to monitor and secure every
endpoint. Insecure devices not only endanger patient privacy but also
jeopardize care delivery. For instance, a compromised infusion pump or
defibrillator could have life-threatening consequences. The Food and Drug
Administration (FDA) has taken steps to address these vulnerabilities through
premarket and post-market cybersecurity guidelines. However, the onus of
ensuring device security often falls into a gray area between manufacturers
and healthcare providers.
The rise of “soft” skills: How GenAI is reshaping developer roles
The successful developer in this evolving landscape will be one who can
effectively combine technical expertise with strong interpersonal skills. This
includes not only the ability to work with AI tools but also the capability to
collaborate with both technical and non-technical stakeholders. After all,
with less of a need for coders to do the low-level, routine work of software
development, more emphasis will be placed on coders’ ability to collaborate
with business managers to understand their goals and create technology
solutions that will advance them. Additionally, the coding that they’ll be
doing will be more complex and high-level, often requiring work with other
developers to determine the best way forward. The emphasis on soft
skills—including adaptability, communication, and collaboration—has become as
crucial as technical proficiency. As the software development field continues
to evolve, it’s clear that the future belongs to those who embrace AI as a
powerful complement to their skills rather than viewing it as a threat. The
coding profession isn’t disappearing—it’s transforming into a role that
demands a more comprehensive skill set, combining technical mastery with
strong interpersonal capabilities.
Top 10 Cybersecurity Trends to Expect in 2025
Zero-day vulnerabilities are still one of the major threats in cybersecurity.
By definition, these faults remain unknown to software vendors and the larger
security community, thus leaving systems exposed until a fix can be developed.
Attackers are using zero-day exploits frequently and effectively, affecting
even major companies, hence the need for proactive measures. Advanced threat
actors use zero-day attacks to achieve goals including espionage and financial
crimes. ... Integrating regional and local data privacy regulations such as
GDPR and CCPA into the cybersecurity strategy is no longer optional. Companies
need to look out for regulations that will become legally binding for the
first time in 2025, such as the EU's AI Act. In 2025, regulators will continue
to impose stricter guidelines related to data encryption and incident
reporting, including in the realm of AI, showing rising concerns about online
data misuse. Decentralized security models, such as blockchain, are being
considered by some companies to reduce single points of failure. Such systems
offer enhanced transparency to users and allow them much more control over
their data. ... Verifying user identities has become more challenging as
browsers enforce stricter privacy controls and attackers develop more
sophisticated bots.
Navigating AI in Aviation: A Roadmap for Risk and Security Management Professionals
The Roadmap for Artificial Intelligence Safety Assurance, recently published
by FAA, recognizes the potential of AI on aviation and emphasizes the need
for safety assurance, industry collaboration and incremental implementation.
This roadmap, combined with other international frameworks, offers a global
framework for managing AI risks in aviation. ... While AI demonstrates the
potential for enhanced operational efficiency, predictive maintenance and
even autonomous flight, these benefits come with significant security and
compliance risks. ... Differentiating between learned AI (static) and
learning AI (adaptive) poses a significant challenge in AI risk management.
The FAA roadmap calls for continuous monitoring and assurance, especially
for learning AI, echoing the need for dynamic risk assessment protocols like
those recommended in NIST-AI-600-1 for managing generative AI models. ...
Incorporating AI in aviation is far from straightforward, and due to human
safety concerns, it involves navigating a constantly evolving landscape of
risks and at times overbearing regulatory requirements. For risk and
security professionals, the key task is to align AI technologies with
operational safety and evolving regulatory requirements.
The Urgent Need for Data Minimization Standards
On one side of the spectrum is the redaction of direct identifiers such as
names, or payment card information such as credit card numbers. On the other
side of the spectrum lies anonymization, where re-identification of
individuals is extremely unlikely. Within the spectrum, we also find
pseudonymization, which, depending on the jurisdiction, often means
something like reversible de-identification Many organizations are keen to
anonymize their data because, if anonymization is achieved, the data falls
outside of the scope of data protection laws as they are no longer
considered personal information. ... We hold that the claim that data
anonymization is impossible is based on a lack of clarity around what is
required for anonymization, with organizations often either wittingly or
unwittingly misusing the term for what is actually a redaction of direct
identifiers. Furthermore, another common claim is that data minimization is
in irresolvable tension with the use of data at a large scale in the machine
learning context. This claim is not only based on a lack of clarity around
data minimization but also a lack of understanding around the extremely
valuable data that often surrounds identifiable information, such as data
about products, conversation flows, document topics, and more.
How CISOs can make smarter risk decisions
Bot detection works by recognizing markers of bad bots, including requests
originating from malicious domains and patterns of behavior exhibited.
Establishing a baseline of normal human web activity and recognizing
anomalous behavior from incoming traffic is at the core of effective bot
detection. ... Unsurprisingly, for businesses focused on managing
users’ money, account takeover and carding attacks are common in the
financial industry. In these instances, cybercriminals try to break into
accounts and steal information from the payments page. As such, the
financial industry has been an early adopter of cybersecurity protocols and
tools to ensure a fully comprehensive and well-funded security program,
while the travel and hospitality industries have not yet made that pivot in
the same way. ... A good CISO makes balanced risk decisions. A bad CISO gets
in the way of helping the company innovate. The combination of industry best
practices and regulation forcing the adoption of robust security tooling and
methodology pushes companies to create a strong baseline to build in
effective protections. However, CISOs must evaluate carefully what assets
they choose to put maximum security measures behind. If you argue that
everything needs that high level of security, you become the CISO who cried
wolf
Developers Are Key to Stopping Rising API Security Threat
Developers and security teams typically share responsibility for ensuring
APIs are secure. “While the security team is ultimately responsible for the
overall security posture of an organization, developers play a key role in
building and managing secure APIs,” Whaley said. “They need to write secure
code and implement security measures during the development phase, such as
input validation, authentication, encryption and access control.” The
security team defines and enforces security policies, he said. They’re also
responsible for establishing governance frameworks and managing tools to
monitor, detect and respond to threats. ... Developers also play an
important role in remediating API security problems, he said. Their job is
to implement fixes and ensure that vulnerabilities are properly addressed.
emediating an incident can include fixing vulnerabilities, deploying patches
and addressing any misconfigurations. But it can also sometimes mean hiring
external help in the form of security consultants, investing in new security
tools and covering any legal and compliance fees, he said. “Additionally,
there are intangible factors to consider, like damage to brand reputation
and loss of customer confidence, which can have a big impact even if they
are harder to quantify,” Whaley added.
Companies Race to Use AI Security Against AI-Driven Threats
First, securing AI by design is crucial, as our customers increasingly rely
on AI in their ecosystems. As a cybersecurity solution provider, our
objective is to ensure our customers are protected when using new
technologies. The second vector involves combating adversaries who use AI to
launch attacks. The rate of these attacks is exponentially faster and more
sophisticated than ever before. To counter this, we must utilize AI to
protect against AI-driven attacks. The third vector focuses on how AI can
benefit security practitioners. By simplifying complex data analysis and
enhancing product interactions, AI can significantly improve the efficiency
and effectiveness of security operations. Solutions such as AI Access
Security, which provides visibility into AI usage within enterprises and
ensures secure AI applications have seen development at 100 customers
already benefiting from our AI security solutions, we see a clear shift in
maturity levels. ... Autonomous SOCs are becoming a reality, driven by two
key factors. First, adversaries are evolving at a pace that outstrips our
ability to scale human resources. Second, there's a shortage of qualified
cybersecurity talent. These dual pressures on both supply and demand -
necessitate technological intervention.
Overcoming modern observability challenges
Observability is crucial for quickly detecting issues and taking corrective
actions to ensure that application performance does not negatively impact
customer experience. With millions of transactions occurring every second,
relying on traditional logic, predefined rules, and human intervention is no
longer sufficient. According to a 2023 Gartner report, applied observability
has emerged as one of the top 10 strategic technology trends, underscoring
the increasing need for using AI to make smarter, more automated solutions
to stay competitive and optimize business operations in real time. Today’s
observability solutions must go beyond static monitoring by incorporating AI
and machine learning to detect patterns, trends, and anomalies. By
automatically identifying outliers and emerging issues, AI-driven systems
reduce the mean time to detect (MTTD) and mean time to resolve (MTTR),
driving efficiency and helping teams address potential problems before they
affect end-users. ... Organizations need an observability solution that is
comprehensive, cost-effective, and intelligent. The Kloudfuse observability
platform is designed to monitor modern cloud-native workloads while
optimizing costs, offering insights into model performance and mitigating
risks.
Managing Software Engineering Teams of Artificial Intelligence Developers
Regardless of its industry, every organization has an AI solution, is
working on AI integration, or has a plan for it in its roadmap. While
developers are being trained in the various technological skills needed for
development, senior leadership must focus on strategies to integrate and
align these efforts with the broader organization. ... Investing in AI alone
will not guarantee success for the company. Avoid making investment
decisions solely based on the Fear of Missing Out. For the business to
thrive in the long run, it must focus on value creation through AI
integration. Follow standard processes and conduct thorough due diligence to
identify where AI can effectively drive value for your product. Collaborate
closely with the product, business, and engineering teams to define the
scope of work and develop a strategic vision that ensures alignment within
the team. It is also crucial to achieve stakeholder alignment, especially
given the complexity of the projects, while setting realistic expectations.
... As an engineering leader, invest in the right skills required for the
project. Empower the team to make the best decisions. Building strong
expertise in the teams and providing learning opportunities for the team by
allowing them to attend learning sessions, conferences, hackathons, etc.
Quote for the day:
“It's failure that gives you the proper perspective on success.”
-- Ellen DeGeneres
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