Quote for the day:
"Failure is the condiment that gives success its flavor." -- Truman Capote
The third leg of the stool: Technology’s role in M&A
The term “technical debt” wasn’t mainstream, making it tough to convey to
lawyers, accountants and executives. Their languages aligned — business,
finance, law — with shared specificity. But IT? We spoke a different dialect,
full of jargon that obscured our business insights. This cultural divide
explained technology’s historical exclusion from M&A. The gap was mine to
bridge. Over time, I learned to translate, framing technical risks in terms of
dollars, downtime and competitive edge. ... Overlap exists with legal and
finance, but IT’s lens is unique: assessing how operations impact data and
systems. Chaotic processes yield chaotic data; effective ones produce reliable
insights. ... “Good decisions on bad data are bad decisions” (me, circa 2007).
Data is an enterprise’s most valuable asset, yet often neglected. Poor data can
cripple; great data accelerates growth. In M&A, I scrutinize quality,
lifecycle management, governance, ownership and analysis. Companies are
typically polarized: exemplary governance or barely functional. Data issues
heavily influence deal pricing — more on that in a future post. ... Critical
during M&A, as deals attract hackers — sometimes derailing them entirely.
With AI-driven threats rising, robust postures are non-negotiable. This warrants
its own article.Navigating the issues that impact data center design today
In the last few years, design considerations have changed significantly. The adoption of high-performance computing (HPC) and artificial intelligence (AI) applications translates into greater power consumption and that requires a rethink of cooling and management. What’s more, it’s increasingly difficult to predict future capacity requirements. ... Modular data center infrastructure can help facilitate zone-based deployments. Many people think of modular data centers as those deployed in ISO shipping containers, but that is only one type. There are also skid-mounted systems and preconfigured enclosures. Preconfigured enclosures can be shells or self-contained units with built-in power, cooling, fire suppression, and physical security. ... Whether building out a new data center or expanding an existing one, organizations should choose sustainable materials. With smart choices, future data centers will be self-sufficient and carbon- and water-neutral and have minimal impact on the local environment.Planning is key These challenges have upped the ante for data center design planning. It’s no longer advisable to build out a simple shell with a raised floor and start adding infrastructure. Your facility must have the necessary power capacity, redundancy, and security to meet your business needs.
Mastering Microservices: Seven Uncommon Strategies for Streamlined Success
Containerization might seem like old news, but there are nuances that can
significantly impact performance and scalability. Containers encapsulate your
microservices, ensuring consistency across environments. Yet, not all container
strategies are created equal. We’ve seen teams struggle when they cram too many
processes into a single container. ... It’s said that you can’t manage what you
can’t measure, and this couldn’t be truer for microservices. With multiple
services running concurrently, effective logging and monitoring become crucial.
Gone are the days of relying solely on traditional log files or single-instance
monitors. We once faced a situation where a subtle bug in a service went
undetected for weeks, causing memory leaks and gradually degrading performance.
Our solution was to implement centralized logging and observability tools like
Prometheus and Grafana. These tools allowed us to aggregate logs from various
services and gain insights through real-time dashboards. ... Security is often
like flossing—everyone knows it’s important, but many neglect it until there’s a
problem. With microservices, security risks multiply. It’s crucial to secure
inter-service communication, protect sensitive data, and ensure compliance with
industry standards.AI Security in the Cloud-Native DevSecOps Pipeline
Because reacting to threats is a lost cause when the attacks themselves are learning and adapting, a proactive stance is essential for survival. This is a mindset embraced by security leaders like Akash Agrawal, VP of DevOps & DevSecOps at LambdaTest, an AI-native software testing platform. He argues for a fundamental shift: “Security can no longer be bolted on at the end,” he explains. “AI allows us to move from reactive scanning to proactive prevention.” This approach means using AI not just to identify flaws in committed code, but to predict where the next one might emerge. ... But architectural flaws are not the only risk. AIʼs drive for automation can also lead to more common security gaps like credential leakage, a problem that Nic Adams, co-founder and CEO of security startup 0rcus, sees growing. He points to AI-backed CI/CD tools that auto-generate infrastructure-as-code and inadvertently create “credential sprawl” by embedding long-lived API keys directly into configuration files. The actionable defense here is to assume AI will make mistakes and build a safety net around it. Teams must integrate real-time secret scanning directly into the pipeline and enforce a strict policy of using ephemeral, short-lived credentials that expire automatically. Beyond specific code vulnerabilities, there is a more strategic gap that AI introduces into the development process itself.Stop using AI for these 9 work tasks - here's why
Every time you give the AI some information, ask yourself how you would feel if
it were posted to the company's public blog or wound up on the front page of
your industry's trade journal. This concern also includes information that might
be subject to disclosure regulations, such as HIPAA for health information or
GDPR for personal data for folks operating in the EU. Regardless of what the AI
companies tell you, it's best to simply assume that everything you feed into an
AI is now grist for the model-training mill. Anything you feed in could later
wind up in a response to somebody's prompt, somewhere else. ... Contracts are
designed to be detailed and specific agreements on how two parties will
interact. They are considered governing documents, which means that writing a
bad contract is like writing bad code. Baaad things will happen. Do not ask AIs
for help with contracts. They will make errors and omissions. They will make
stuff up. Worse, they will do so while sounding authoritative, so you're more
likely to use their advice. ... But when it comes time to ask for real advice
that you plan on considering as you make major decisions, just don't. Let's step
away from the liability risk issues and focus on common sense. First, if you're
using something like ChatGPT for real advice, you have to know what to ask. If
you're not trained in these professions, you might not know.The Evolution of the DBA—More Than Just a Keeper of Databases
Automation has dramatically changed database administration. Routine tasks—such as performance tuning, index management, and backup scheduling—are increasingly handled by AI-driven database tools. Solutions such as Oracle Autonomous Database, Db2 AI for SQL, and Microsoft Azure SQL’s Intelligent Query Processing promise self-optimizing, self-healing databases. While this might sound like a threat to DBAs, it’s actually an opportunity. Instead of focusing on routine maintenance, DBAs can now shift their efforts toward higher-value tasks including data architecture, governance, and security. ... Organizations are no longer tied to a single database platform. With multi-cloud and hybrid cloud strategies becoming the norm, DBAs must manage data across on-premises systems, cloud-native databases, and hybrid architectures. The days of being a single-platform DBA (e.g., only working with one DBMS) are coming to an end. Instead, cross-platform expertise is now a necessity. Knowing how to optimize for multiple platforms and database systems—for example, AWS RDS, Google Cloud Spanner, Azure SQL, and on-prem Db2, Oracle, and PostgreSQL—is more and more a core part of the DBA’s job description. ... With the explosion of data regulations and industry-specific mandates, compliance has become a primary concern for DBAs.The global challenge of achieving cyber resilience
The barriers to effective cybersecurity include familiar suspects such as
budgetary and resource limitations, the increasing complexity of modern systems
and challenge of keeping up with rapidly evolving cyber threats. However,
topping the list of challenges for many organisations is the ongoing shortage of
cybersecurity skills. A recent Cybersecurity Workforce Study from ISC2 found
that, although the size of the global cybersecurity workforce increased to 5.5
million workers in 2023 (a rise of 9% over a single year), so did the gap
between supply and demand, which rose by 13% over the same period.
Unfortunately, it’s more than just a numbers gap. The study also found that the
skills gap is an even greater concern, with respondents saying the lack of
necessary skills was a bigger factor making their organisations vulnerable. It’s
clear the current approach is flawed. The grand plans that governments have for
cybersecurity will require significant uplifts to security programs, including
major improvements in developer upskilling, skills verification and guardrails
for artificial intelligence tools. Organisations also need to modernise their
approach by implementing pathways to upskilling that use deep data insights to
provide the best possible skills verification. They need to manage and mitigate
the inherent risks that developers with low security maturity bring to the
table.
Social engineering becomes strategic threat as OT sector faces phishing, deepfakes, and AI deception risks
With the expanding IT/OT footprint, the attack surface is increasingly providing attackers additional opportunities to compromise targets by stealing credentials, impersonating trusted insiders, and moving laterally from one system to another inside the network. AI-driven phishing, voice cloning, and deepfake-enabled pretexting are lowering the barrier to entry, enabling cyber adversaries to deploy powerful tools that have the potential to erode the reliability of human judgment across critical infrastructure installations. Microsoft security researchers warn that a single compromise, say via a contractor’s infected laptop, can breach previously isolated OT systems, turning them into a breach gateway. While phishing and identity theft are now common access tools, the impact in OT environments is much worse. ... AI-driven deception is rapidly reshaping the social engineering landscape. Attackers are using voice cloning and deepfake technology to impersonate executives with unnerving accuracy. Qantas recently fell victim to a similar scheme, where an AI-powered ‘vishing’ attack compromised the personal data of up to six million customers. These incidents highlight how artificial intelligence has lowered the barrier for convincing, high-impact fraud. Across OT environments, such as energy distribution or manufacturing plants, the impact of social engineering goes way beyond stolen funds or data.When cloud growth outpaces control, waste follows
Access to data does not guarantee accountability. Many organizations have
detailed cost reporting but continue to struggle with cloud waste. The issue
here shifts from one of visibility towards one of proximity. Our data shows 59%
of organizations have a FinOps team that does some or all cloud cost
optimization tasks, yet in many cases, these teams still sit at the edge of
delivery. So, while they can surface issues, they are often too removed from
daily operations to intervene effectively. The most effective models integrate
cost ownership into delivery itself. This means that engineering leads, platform
teams and product owners have oversight to take action before inefficiencies
take hold. As a result, when these roles are supported with relevant reporting
and shared financial metrics, cost awareness becomes a natural part of the
decision-making process. This makes it easier to adjust workloads, retire
underutilized services, and optimize environments in-flight, rather than in
hindsight. ... Control is easiest to build before complexity sets in. The longer
organizations delay embedding structure into cloud governance, the harder it
becomes to retrofit later. Inconsistent tagging, ambiguous ownership and manual
reporting all take time to correct once they are entrenched.
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