Daily Tech Digest - May 06, 2025


Quote for the day:

"Limitations live only in our minds. But if we use our imaginations, our possibilities become limitless." --Jamie Paolinetti


A Primer for CTOs: Taming Technical Debt

Taking a head-on approach is the most effective way to address technical debt, since it gets to the core of the problem instead of slapping a new coat of paint over it, Briggs says. The first step is for leaders to work with their engineering teams to determine the current state of data management. "From there, they can create a realistic plan of action that factors in their unique strengths and weaknesses, and leaders can then make more strategic decisions around core modernization and preventative measures." Managing technical debt requires a long-term view. Leaders must avoid the temptation of thinking that technical debt only applies to legacy or decades old investments, Briggs warns. "Every single technology project has the potential to add to or remove technical debt." He advises leaders to take a cue from medicine's Hippocratic Oath: "Do no harm." In other words, stop piling new debt on top of the old. ... Technical debt can be useful when it's a conscious, short-term trade-off that serves a larger strategic purpose, such as speed, education, or market/first-mover advantage, Gibbons says. "The crucial part is recognizing it as debt, monitoring it, and paying it down before it becomes a more serious liability," he notes. Many organizations treat technical debt as something they're resigned to live with, as inevitable as the laws of physics, Briggs observes. 


AI agents are a digital identity headache despite explosive growth

“AI agents are becoming more powerful, but without trust anchors, they can be hijacked or abused,” says Alfred Chan, CEO of ZeroBiometrics. “Our technology ensures that every AI action can be traced to a real, authenticated person—who approved it, scoped it, and can revoke it.” ZeroBiometrics says its new AI agent solution makes use of open standards and technology, and supports transaction controls including time limits, financial caps, functional scopes and revocable keys. It can be integrated with decentralized ledgers or PKI infrastructures, and is suggested for applications in finance, healthcare, logistics and government services. The lack of identity standards suited to AI agents is creating a major roadblock for developers trying to address the looming market, according to Frontegg. That is why it has developed an identity management platform for developers building AI agents, saving them from spending time building ad-hoc authentication workflows, security frameworks and integration mechanisms. Frontegg’s own developers discovered these challenges when building the company’s autonomous identity security agent Dorian, which detects and mitigates threats across different digital identity providers. “Without proper identity infrastructure, you can build an interesting AI agent — but you can’t productize it, scale it, or sell it,” points out Aviad Mizrachi, co-founder and CTO of Frontegg.


Rethinking digital transformation for the agentic AI era

Most CIOs already recognize that generative AI presents a significant evolution in how IT departments can deliver innovations and manage IT services. “Gen AI isn’t just another technology; it’s an organizational nervous system that exponentially amplifies human intelligence,” says Josh Ray, CEO of Blackwire Labs. “Where we once focused on digitizing processes, we’re now creating systems that think alongside us, turning data into strategic foresight. The CIOs who thrive tomorrow aren’t just managing technology stacks; they’re architecting cognitive ecosystems where humans and AI collaborate to solve previously impossible challenges.” IT service management (ITSM) is a good starting point for considering gen AI’s potential. Network operation centers (NOCs) and site reliability engineers (SREs) have been using AIOps platforms to correlate alerts into time-correlated incidents, improve the mean time to resolution (MTTR), and perform root cause analysis (RCA). As generative and agentic AI assists more aspects of running IT operations, CIOs gain a new opportunity to realign IT ops with more proactive and transformative initiatives. ... “Opportunities such as gen AI for hotfix development and predictive AI to identify, correlate, and route incidents for improved incident response are transforming our business, resulting in improved customer satisfaction, revenue retention, and engineering efficiency.”


Strengthening Software Security Under the EU Cyber Resilience Act: A High-Level Guide for Security Leaders and CISOs

One of the hardest CRA areas for organizations to get a handle on is knowing and proving where appropriate controls and configurations are in place vs. where they’re lacking. This lack of visibility often leads to underutilized licenses, unchecked areas of product development, and the potential for unauthorized access into sensitive areas of the development environment. One of the ways security-conscious organizations are combating this is through the creation of “paved pathways” that include very specific technology and security tooling to be utilized across all their development environments, but this often requires extreme vigilance of deviations within those environments and very few ways to automate the adherence to those standards. Legit Security not only automatically inventories and details what and where controls exist within an SDLC so you can ensure 100% coverage of your application portfolio, but we also analyze all of the configurations throughout the entirety of the build process to find any that could allow for supply chain attacks or unauthorized access to SCMs or CI/CD systems. This ensures that your teams are using secure defaults and putting appropriate guardrails into development workflows. This also automates baseline enforcement, configuration management, and quick resets to a known safe state when needed.


Observability 2.0? Or Just Logs All Over Again?

As observability solutions have ostensibly become more mature over the last 15 years, we still see customers struggle to manage their observability estates, especially with the growth of cloud native architectures. So-called “unified” observability solutions bring tools to manage the three pillars, but cost and complexity continue to be major pain points. Meanwhile, the volume of data has kept rising, with 37% of enterprises ingesting more than a terabyte of log data per day. Legacy logging solutions typically deal with the problems of high data volume and cardinality through short retention windows and tiered storage — meaning that data is either thrown away after a fairly short period of time or stored in frozen tiers where it goes dark. Meanwhile, other time series or metric databases take high-volume source data, aggregate it into metrics, then discard the underlying logs. Finally, tracing generates so much data that most traces aren’t even stored in the first place. Head-based sampling retains a small percentage of traces, typically random, while tail-based sampling allows you to filter more intelligently but at the cost of efficient processing. And then traces are typically discarded after a short period of time. There’s a common theme here: While all of the pillars of observability provide different ways of understanding and analyzing your systems, they all deal with the problem of high cardinality by throwing data away.


What it really takes to build a resilient cyber program

A good place to begin is the ‘Identify’ phase from NIST’s Incident Response guide. You need to identify all of your risks, vulnerabilities, and assets. Prioritize them and then determine the best way to protect and detect threats against those assets. Assets not only include physical things like laptops and phones, but also anything that is in a Cloud Service Provider, SaaS applications, and digital items like domain names. Determine the threats, risks and vulnerabilities to those assets. Prioritize them and determine how your organization is going to protect and monitor them. Most organizations don’t have a very good idea of what they actually own, which is why they tend to be reactive and waste time on actions that do not apply to them. How often has a security analyst been asked if a recently disclosed zero-day affects the company? They perform the scans and pull in data manually only to discover they don’t run that piece of software or hardware. ... Many organizations use a red team exercise to try and blame someone or group for a deficiency or even to score an internal political point. That will never end well for anyone. The name of the game is improvement in your security posture and these help identify areas of weakness. There might be things that don’t get fixed immediately, or maybe ever, but knowing that the gap exists is the critical first step. 


Top tips for successful threat intelligence usage

“The value of threat intelligence is directly tied to how well it is ingested, processed, prioritized, and acted upon,” wrote Cyware in their report. This means a careful integration into your existing constellation of security tools so you can leverage all your previous investment in your acronyms of SOARs, SIEMs and XDRs. According to the Greynoise report “you have to embed the TIP into your existing security ecosystem, making sure to correlate your internal data and use your vulnerability management tools to enhance your incident response and provide actionable analytics.” The keyword in that last sentence is actionable. Too often threat intel doesn’t guide any actions, such as kicking off a series of patches to update outdated systems, or remediation efforts to firewall a particular network segment or taking offline an offending device. ... Part of the challenge here is to prevent siloed specialty mindsets from making the appropriate remedial measures. “I’ve seen time and time again when the threat intel or even the vulnerability management team will send out a flash notification about a high priority threat only for it to be lost in a queue because the threat team did not chase it up. It’s just as important for resolver groups to act as it is for the threat team to chase it,” Peck blogged.


How empathy is a leadership gamechanger in a tech-first workplace

Empathy isn’t just about creating a feel-good workplace—it’s a powerful driver of innovation and performance. When leaders lead with empathy, they unlock something essential: a work culture where people feel safe to speak up, take risks, and bring their boldest ideas to life. That’s where real progress happens. Empathy also enhances productivity, employees who feel valued and supported are more motivated to perform at their highest potential. Research shows that organisations led by empathetic leaders experience a 20% increase in customer loyalty, underscoring the far-reaching impact of a people-first approach. When employees thrive, so do customer relationships, business outcomes, and overall organisational growth. In India, where workplace dynamics are often shaped by hierarchical structures and collectivist values, empathetic leadership can be transformative. By prioritising open communication, recognition, and personal development, leaders can strengthen employee morale, increase job satisfaction, and drive long-term loyalty. ... In a tech-first world, empathy isn’t a nice-to-have, it’s a leadership gamechanger. When leaders lead with heart and clarity, they don’t just inspire people, they unlock their full potential. Empathy fuels trust, drives innovation, and builds workplaces where people and ideas thrive. 


Analyzing the Impact of AI on Critical Thinking in the Workplace

Instead of generating content from scratch, knowledge workers increasingly invest effort in verifying information, integrating AI-generated outputs into their work, and ensuring that the final outputs meet quality standards. What is motivating this behavior? Some explanations for these trends could be to enhance work quality, develop professional AI skills, laziness, and the desire to avoid negative outcomes like errors. For example, someone who is not very proficient in the English language could use GenAI to make their emails sound a lot more natural and avoid any potential misunderstandings. On the flipside, there are some drawbacks to using GenAI. These include overreliance on GenAI for routine or lower-stakes tasks, time pressures, limited awareness of potential AI pitfalls, and challenges in improving AI responses. ... The findings suggest that GenAI tools can reduce the perceived cognitive load for certain tasks. However, they find that GenAI poses risks to workers’ critical thinking skills by shifting their roles from active problem-solvers to AI output overseers who must verify and integrate responses into their workflows. Once again (and this can not be emphasized enough) the study underscores the need for designing GenAI systems that actively support critical thinking. This will ensure that efficiency gains do not come at the expense of developing essential critical thinking skills.


Harnessing Data Lineage to Enhance Data Governance Frameworks

One of the most immediate benefits is improved data quality and troubleshooting. When a data quality issue arises, data lineage’s detailed trail can help you to quickly identify where the problem originated, so that you can fix errors and minimize downtime. Data lineage also enables better planning, since it allows you to run more effective data protection impact analysis. You can map data dependencies to assess how changes like system upgrades or new data integrations might affect your overall data integrity. This is especially valuable during migrations or major updates, as you can proactively mitigate any potential disruptions. Furthermore, regulatory compliance is also greatly enhanced through data lineage. With a complete audit trail documenting every data movement and transformation, organizations can more easily demonstrate compliance with regulations like GDPR, CCPA, and HIPAA. ... Developing a comprehensive data lineage framework can take substantial time, not to mention significant funds. In addition to the various data lineage tools, you might also need to have dedicated hosting servers, depending on the level of compliance needed, or to hire data lineage consultants. Mapping out complex data flows and maintaining up-to-date lineage in a data landscape that’s constantly shifting requires continuous attention and investment.

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