Daily Tech Digest by Kannan Subbiah: ITOps

Showing posts with label ITOps. Show all posts

Daily Tech Digest - July 29, 2026

Quote for the day:

“The most successful founders are relentless about pushing through obstacles.” -- Sam Altman

🎧 Listen to the audio debrief on YouTube

Duration: 20 mins • Perfect for listening on the go.

CISA shares advice on isolating vital systems during cyberattacks

The U.S. Cybersecurity and Infrastructure Security Agency, alongside the FBI and international partners, has released new guidance to help critical infrastructure organizations successfully isolate their vital systems during a severe cyberattack. Titled "CI Fortify," this advisory provides practical steps for operators of essential services, like water treatment, power grids, and telecommunications, to confidently disconnect their core operational technology from corporate and internet-facing networks when a serious threat is detected. With state-sponsored groups and cybercriminals increasingly targeting these vital sectors for extortion and disruption, having a secure plan to safely sever network connections is crucial. The guidance recommends that organizations first carefully identify the absolute minimum systems needed to keep services running smoothly, and then map out every single connection to less trusted external networks. From there, they should establish predetermined isolation points where systems can be fully disconnected. While physical isolation offers the absolute strongest protection, the agencies completely acknowledge it may not always be feasible, instead suggesting graduated isolation and strict network controls as reliable alternatives. Additionally, organizations are urged to test their isolation procedures thoroughly and always keep offline paper copies of their detailed plans. Finally, the advisory reminds operators to thoroughly prepare for the expected challenges of manually running systems while completely disconnected.

Why DORA Metrics Are More Important Than Ever

As artificial intelligence tools help software teams write code at unprecedented speeds, organizations face a growing risk of deploying flawed software just as quickly. The temptation is to measure progress through activity-based metrics, such as the volume of code generated, tickets closed, or prompts submitted to AI assistants. However, this approach mistakes effort for actual value. To ensure that speed does not compromise quality, DORA metrics are more vital than ever. The four classic DORA metrics are deployment frequency, lead time for changes, change failure rate, and mean time to restore. Together, they offer a balanced view of both speed and stability. Unlike raw activity counts, these outcome-focused measures reveal whether an organization's software delivery system is genuinely healthy. While AI can accelerate development, counting lines of code or prompt submissions only exposes how superficial those metrics are. If AI integration is successful, it will be reflected in shorter lead times, more reliable deployments, and faster recovery from inevitable failures. Ultimately, AI cannot automatically fix a weak delivery process; it might only amplify existing gaps. Relying on DORA metrics helps technology leaders distinguish mere motion from actual progress, ensuring the ultimate goal remains delivering valuable, reliable software rather than just generating more code.

An AI agent can pass every safety check and still leak secrets

Security researcher Elad Meged recently demonstrated that default AI agent setups from major providers like Anthropic, Google, and OpenAI can quietly leak sensitive information, even when they pass routine safety checks. The fundamental problem lies not within the AI models themselves, but within the surrounding structures that connect these models to file systems, basic commands, and external network requests. When agents operate without direct human oversight, this connecting framework acts as the primary security barrier. Meged discovered that significant risks emerge during the handoffs between different operational stages. For instance, a specific command might be approved because it looks like a safe reading action, but if its output is later published without an additional check, it forms a complete path for data theft. While companies have patched isolated flaws and paid out rewards, these fixes frequently miss the deeper structural weakness. To properly secure these environments, trust must be constantly rechecked at every point of a process, rather than leaning on a single initial permission. Organizations using these automated AI agents in active environments are highly advised to closely trace the full journey of an agent's output to confirm that safe beginnings do not lead to unintended data exposures later.

Beyond Monitoring: Why IT Operations Must Evolve into Decision Operations

As technology systems grow more complicated, traditional ways of watching them are no longer enough. For years, technology teams relied on basic tracking tools that simply sent an alert when a server went offline or a website slowed down. While knowing there is a problem is helpful, these basic alerts often create too much noise. When dozens of alarms go off at the same time, it is hard for teams to know which issue to fix first or what actually caused the failure. Because of this, technology operations must shift from simply gathering data to actively supporting choices. Instead of just showing charts and red lights, modern operations focus on pulling all the separate clues together to provide clear, direct advice. By linking the symptoms directly to their root causes, this approach allows teams to understand the context of a problem immediately. Moving toward a model focused on making decisions helps teams reduce the time spent guessing and investigating. They can fix problems faster, prevent minor issues from becoming major outages, and spend more time improving systems rather than just keeping them running. Ultimately, it is about shifting the focus from watching screens to taking effective action that keeps the business running smoothly.

AI Costs Continue to Rise Despite Falling Token Prices

Despite the price of AI tokens dropping by roughly 98 percent since early 2024, enterprise computing bills continue to climb rapidly. The primary reason for this paradox is the shift from basic chatbots to autonomous agents. While a simple chatbot handles a single prompt, modern AI agents break tasks into multiple steps, such as planning, retrieving information, and verifying data, which consumes significantly more tokens per interaction. Furthermore, many organizations are feeding messy, unstructured files directly into their systems. When models process formats like standard PDFs, they waste vast amounts of computing power just trying to understand the document layout before extracting any useful facts. In these advanced workloads, the actual output often represents only a small fraction of the total tokens used, with the rest lost to processing overhead. Beyond the basic token fees, companies face substantial hidden expenses related to cleaning data, resolving inconsistent internal terminology, and integrating older systems. Experts strongly advise businesses to clean, organize, and structure their data before feeding it into language models. By addressing these foundational data issues upfront, organizations can reduce unnecessary processing waste, lower their overall costs, and ensure their AI tools operate much more effectively in the long run.

The compounding enterprise

The recent record-breaking copyright settlement against a major AI company highlights a growing risk for businesses: relying heavily on generic AI models trained on unverified or contested data. This approach creates hidden legal exposure and relies on a foundational asset that is quickly becoming a commodity. To build a lasting operational advantage, organizations must shift away from simply renting generic intelligence and instead focus on compounding their own. The key is creating an internal cycle where every interaction steadily improves the system's underlying data, and better data improves future decisions. Rather than feeding systems with unverified information, which only multiplies errors, companies should ground their AI tools in carefully curated, human-authored knowledge. This verified approach ensures that outputs can be traced directly to their original sources, solving compliance and governance issues by design. Over time, this system acts as a reliable institutional memory that captures employee expertise before it leaves the company, preventing the need to relearn past lessons. As the system continuously learns from verified outcomes, the cost of making accurate decisions drops while the value of proprietary knowledge increases. Organizations can begin this transition by inventorying their current AI tools, requiring clear sourcing for automated decisions, and testing a governed system in employee training.

Claude AI Just Cracked a Post-Quantum Test Scheme and Found a Faster 7-Round AES Attack

Anthropic recently announced that its AI model, Claude Mythos Preview, successfully developed novel attacks against two encryption schemes. First, the AI created an end-to-end key recovery attack for HAWK-256, which is a challenge parameter of a lattice-based signature scheme currently under review by the National Institute of Standards and Technology. By identifying a previously unused symmetry within the mathematical structure of the scheme, the model significantly reduced the expected effort required to recover cryptographic keys. Second, the AI accelerated an existing attack on a reduced, seven-round version of AES-128 by a factor of 200 to 800. It achieved this by discovering a new technique, known as the Möbius Bridge, which entirely eliminates a time-consuming guessing step. While these findings represent notable progress in AI-driven cryptography research, they do not pose any immediate threat to current production systems. The HAWK attack targets a smaller test parameter rather than full-strength versions, and the AES breakthrough applies only to a reduced-round cipher while still requiring an impractical amount of data. The research cost approximately $100,000 in API usage and took the AI a few days to generate, though human researchers spent nearly a month verifying the mathematical correctness of the model's work.

The Hard-Stop Rule: From 3 HCM Monoliths to 120 Domain Microservices

A pull-based migration strategy represents a shift in how teams approach large-scale system updates, moving away from forced, all-at-once transitions. Instead of a central team pushing changes to every downstream service simultaneously, the new system or API is made available alongside the old one. Client teams are then empowered to pull the updates and migrate at their own pace. This approach significantly reduces the risk of widespread outages because the migration happens incrementally rather than in a single, high-stakes cutover event. By decentralizing the transition effort, organizations can avoid painful bottlenecks where a single team is responsible for coordinating every moving part. Individual teams can plan their migration around their own product cycles, testing thoroughly before fully committing to the new architecture. If issues arise during a team's transition, the impact is kept to just that specific service, making rollbacks far less disruptive. Furthermore, this method naturally encourages better communication and documentation, as the central team must provide clear guidelines for clients to adopt the new system independently. Ultimately, a pull-based migration fosters a more resilient and manageable evolution of your software architecture, balancing the need for technical progress with the practical realities of team capacity and system stability.

AI is a top priority, but there is also distrust about use in cybersecurity

According to a recent report by Arctic Wolf, while artificial intelligence is a major priority for many organizations, security leaders still hesitate to trust it fully for autonomous defense. Although a large majority of respondents note that AI improves their overall security by sorting through and analyzing large amounts of data, only a small fraction are comfortable letting it make decisions on its own. This caution stems from concerns over data privacy, lack of transparency, and the potential for large-scale errors. Interestingly, despite frequent security incidents, most security leaders remain highly confident in their human teams' ability to handle threats. Christopher Fielder of Arctic Wolf suggests this high confidence might be more about projecting assurance than reflecting reality. To bridge the gap between human oversight and AI assistance, organizations need a balanced approach. This involves creating clear acceptable use guidelines to define exactly how AI can and should be used within the company. Furthermore, it is important to provide comprehensive education for staff so they understand both the strengths and the limitations of these new tools. By treating AI as a practical resource rather than a magical cure, companies can better integrate it into their defenses and improve their response to increasingly complex threats.

IoT Sector Given Final EU Cyber Resilience Act Guidance

The recent official guidance on the European Union’s Cyber Resilience Act outlines critical new cybersecurity requirements for manufacturers in the Internet of Things sector. Starting on September 11, 2026, companies that sell products with digital elements in the EU must adhere to strict reporting rules. When a manufacturer discovers an actively exploited vulnerability or a severe security incident, they have just 24 hours to file an early warning and 72 hours to submit a detailed notification to the relevant authorities through a central platform. This upcoming deadline represents the first major regulatory phase of the act, meaning businesses must quickly establish processes for tracking software dependencies and handling vulnerability disclosures. Furthermore, the guidance details that by December 11, 2027, the remaining obligations of the act will take effect. These include maintaining a software bill of materials, designing products with security built in from the start, and obtaining appropriate conformity assessments before bringing products to market. Failure to comply could result in substantial fines or forced product recalls. The overall objective is to hold hardware and software creators accountable for the security of their products throughout their entire life cycle, replacing fragmented national rules with a single, clear standard across the European market.

Daily Tech Digest - September 18, 2025

Quote for the day:

"When your life flashes before your eyes, make sure you’ve got plenty to watch.” -- Anonymous

The new IT operating model: cloud-managed networking as a strategic lever

Enterprises are navigating an environment where the complexity of IT is increasing exponentially. Hybrid work requires consistent connectivity across homes, offices, and campuses. Edge computing and IoT generate massive volumes of data at distributed sites. Security risks escalate as the attack surface grows. Traditional, hardware-centric approaches leave IT teams struggling to keep up. Managing dozens or hundreds of controllers, patching firmware manually, and troubleshooting issues site by site is not sustainable. Cloud-managed networking changes that equation. By centralizing management, applying AI-driven intelligence, and extending visibility across distributed environments, it enables IT to shift from reactive firefighting to proactive strategy. ... Enterprises adopting cloud-managed networking are making a decisive shift from complexity to clarity. Success requires more than technology alone. It demands a partner that understands how to translate advanced capabilities into measurable business outcomes. ... Cloud-managed networking is not just another IT trend. It is the operating model that will define enterprise technology for the next decade. By elevating the network from infrastructure to strategy, it enables organizations to move faster, stay secure, and innovate with confidence.

Why Shadow AI Is the Next Big Governance Challenge for CISOs

In many respects, shadow AI is a subset of a broader shadow IT problem. Shadow IT is an issue that emerged more than a decade ago, largely emanating from employee use of unauthorized cloud apps, including SaaS. Lohrmann noted that cloud access security broker (CASB) solutions were developed to deal with the shadow IT issue. These tools are designed to provide organizations with full visibility of what employees are doing on the network and on protected devices, while only allowing access to authorized instances. However, shadow AI presents distinct challenges that CASB tools are unable to adequately address. “Organizations still need to address other questions related to licensing, application sprawl, security and privacy policies, procedures and more ..,” Lohrmann noted. A key difference between IT and AI is the nature of data, the speed of adoption and the complexity of the underlying technology. In addition, AI is often integrated into existing IT systems, including cloud applications, making these tools more difficult to identify. Chuvakin added, “With shadow IT, unauthorized tools often leave recognizable traces – unapproved applications on devices, unusual network traffic or access attempts to restricted services. Shadow AI interactions, however, often occur entirely within a web browser or personal device, blending seamlessly with regular online activity or not leaving any trace on any corporate system at all.”

Cisco strengthens integrated IT/OT network and security controls

Melding IT and OT networking and security is not a new idea, but it’s one that has seen growing attention from Cisco. ... Cisco also added a new technology called AI-powered asset clustering to its Cyber Vision OT management suite. Cyber Vison keeps track of devices connected to an industrial network, builds a real-time map of how these devices talk to each other and to IT systems, and can detect abnormal behavior, vulnerabilities, or policy violations that could signal malware, misconfigurations, or insider threats, Cisco says. ... Another significant move that will help IT/OT integration is the planned integration of the management console for Cisco’s Catalyst and Meraki networks. That combination will allow IT and OT teams to see the same dashboard for industrial OT and IT enterprise/campus networks. Cyber Vision will feeds into the dashboard along with other Cisco management offerings such as ThousandEyes, which gives customers a shared inventory of assets, traffic flows and security. “What we are focusing on is helping our customers have the secure networking foundation and architecture that lets IT teams and operational teams kind of have one fabric, one architecture, that goes from the carpeted spaces all the way to the far reaches of their OT network,” Butaney said.

Global hiring risks: What you need to know about identity fraud and screening trends

Most organizations globally include criminal record checks in their pre-employment screening. Employment and education verifications are also common, especially in EMEA and APAC. ... “Employers that fail to strengthen their identity verification processes or overlook recurring discrepancy patterns could face costly consequences, from compliance failures to reputational harm,” said Euan Menzies, President and CEO of HireRight. ... More than three-quarters of businesses globally found at least one discrepancy in a candidate’s background over the past year. Thirteen percent reported finding one discrepancy for every five candidates screened. Employment verification remains the area where most inconsistencies are discovered, especially in APAC and EMEA. These discrepancies range from minor errors like incorrect dates to more serious issues such as fabricated job histories. ... Companies are increasingly adopting post-hire screening to address risks that emerge after someone is hired. In North America, only 38 percent of companies now say they do no post-hire screening, a sharp drop from 57 percent last year. Common post-hire checks include driver monitoring and periodic rescreening for regulated roles. These efforts help companies catch new issues such as undisclosed criminal activity, changes in legal eligibility to work, or evolving insider threats.

Doomprompting: Endless tinkering with AI outputs can cripple IT results

Some LLMs appear to be designed to encourage long-lasting conversation loops, with answers often spurring another prompt. ... “When an individual engineer is prompting an AI, they get a pretty good response pretty quick,” he says. “It gets in your head, ‘That’s pretty good; surely, I could get to perfect.’ And you get to the point where it’s the classic sunk-cost fallacy, where the engineer is like, ‘I’ve spent all this time prompting, surely I can prompt myself out of this hole.’” The problem often happens when the project lacks definitions of what a good result looks like, he adds. “Employees who don’t really understand the goal they’re after will spin in circles not knowing when they should just call it done or step away,” Farmer says. “The enemy of good is perfect, and LLMs make us feel like if we just tweak that last prompt a little bit, we’ll get there.” ... Govindarajan has seen some IT teams get stuck in “doom loops” as they add more and more instructions to agents to refine the outputs. As organizations deploy multiple agents, constant tinkering with outputs can slow down deployments and burn through staff time, he says. “The whole idea of doomprompting is basically putting that instruction down and hoping that it works as you set more and more instructions, some of them contradicting with each other,” he adds. “It comes at the sacrifice of system intelligence.”

Vanishing Public Record Makes Enterprise Data a Strategic Asset

“We are rapidly running out of public data that is credible and usable. More and more enterprises will start to assign value to their data and go beyond partnerships to monetize it. For example, wind measurements captured by a wind turbine company could be helpful to many businesses that are not competitors,” said Olga Kupriyanova, principal consultant of AI and data engineering at ISG. ... "We’re entering a defining moment in AI where access to reliable, scalable, and ethical data is quickly becoming the central bottleneck, and also the most valuable asset. As legal and regulatory pressure tightens access to public data, due to copyright lawsuits, privacy concerns, or manipulation of open data repositories, enterprises are being forced to rethink where their AI advantage will come from,” said Farshid Sabet, CEO and co-founder at Corvic AI, developer of a GenAI management platform. ... The economic consequences of such data loss are already visible. Analysts estimate that U.S. public data underpinned nearly $750 billion of business activity as recently as 2022, according to the Department of Commerce. The loss of such data blinds companies that build models for everything from supply chain forecasting to investment strategy and predictions.

The Architecture of Responsible AI: Balancing Innovation and Accountability

The field of AI governance suffers from what Mackenzie et al reaffirm as the “principal-agent problem,” where one party (the principal) delegates tasks to another party (the agent). But their interests are not perfectly aligned, leading to potential conflicts and inefficiencies. ... Architects occupy a unique position in this landscape. Unlike regulators who may impose constraints post-design, architects work at the intersection of possibility and constraint. They must balance competing requirements, such as performance and privacy, efficiency and equity, speed and safety, within coherent system designs. Every architectural decision must embed values, priorities, and assumptions about how systems should behave. ... current AI guidance suffers from systematic weaknesses: evidence quality is sacrificed for speed, commercial interests masquerade as objective advice, and some perspectives dominate while broader stakeholder voices remain unheard ... Architects, being well-placed to bridge the gap between strategy and technology, hold a key role in establishing the principles that govern how systems behave, interact, and evolve. In the context of AI, this principle set extends beyond technical design. It encompasses the ethical, social, and legal aspects as well. .

AI will make workers ‘busier in the future’ – so what’s the point exactly?

“I have to admit that I’m afraid to say that we are going to be busier in the future than now,” he told host Liz Claman. “And the reason for that is because a lot of different things that take a long time to do are now faster to do. I’m always waiting for work to get done because I’ve got more ideas.” ... “The more productive we are, the more opportunity we get to pursue new ideas,” Huang continued. Reading between the lines here, it seems the so-called efficiency gains afforded by AI will mean workers have more work dumped in their laps – onto the next task, no rest for the wicked, etc. Huang’s comments run counter to the prevailing sentiment among big tech executives on exactly what AI will deliver for both enterprises and individual workers. ... We’ve all read the marketing copy and heard it regurgitated by tech leaders on podcasts and keynote stages – AI will allow us to focus on the “more rewarding” aspects of our jobs. They’ve never fully explained what this entails, or how it will pan out in the workplace. To be quite honest, I don’t think they know what it means. Marketing probably made it up and they’ve stuck with it. ... Will we be busier spending time on those rewarding aspects of our jobs? I have to say, I’m doubtful. The reality is that workers will be pulled into other tasks and merely end up drowning in the same cumbersome workloads they’ve been dealing with since the pandemic.

Building Safer Digital Experiences Through Robust Testing Practices

Secure software testing forms the bedrock of resilient applications, proactively uncovering flaws before they become critical. Early testing practices can significantly reduce risks, costs, and exposure to threats. According to Global Market Insights, the growing number and size of data breaches have increased the need for security testing services. Organizations that heavily use security AI and automation save an average of USD 1.76 million compared to those that don’t. About 51% plan to increase their security spending. Early integration of techniques like Static Application Security Testing (SAST) can detect vulnerabilities in existing code. It can also help to fix bugs during development. ... Organizations must verify that their systems handle personal data securely and comply with global regulations like GDPR and CCPA. Testing ensures sensitive information is protected from leaks or unauthorized use. Americans are highly concerned about how companies use their private data. ... Stress testing evaluates how applications perform under extreme loads. It helps identify potential failures in scalability, response times, and resource management. Vulnerability assessments concentrate on uncovering security gaps. Verified Market Reports notes that, after recent financial crises, governments are putting stronger emphasis on stress testing.

Prompt Engineering Is Dead – Long Live PromptOps

PromptOps is gaining traction rapidly because it has the potential to address major challenges in the use of LLMs, such as prompt drift and suboptimal output. Yet incorporating PromptOps effectively into an organization is far from simple, requiring a structured and clear process, the right tools, and a mindset that enables collaboration and effective centralization. Digging deeper into what PromptOps is, why it is needed, and how it can be implemented effectively can help companies to find the right approach when incorporating this methodology for improving their LLM applications usage. ... Before PromptOps is implemented, an organization typically has prompts scattered across multiple teams and tools, with no structured management in place. The first stage of implementing PromptOps involves gathering every detail on LLM applications usage within an organization. It is essential to understand precisely which prompts are being used, by which teams, and with which models. The next stage is to build consistency into this practice by incorporating versioning and testing. Adding secure access control at this stage is also important, in order to ensure only those who need it have access to prompts. With these practices in place, organizations will be well-positioned to introduce cross-model design and embed core compliance and security practices into all prompt crafting.

Daily Tech Digest - April 24, 2025

Quote for the day:

“Remember, teamwork begins by building trust. And the only way to do that is to overcome our need for invulnerability.” -- Patrick Lencioni

Algorithm can make AI responses increasingly reliable with less computational overhead

The algorithm uses the structure according to which the language information is organized in the AI's large language model (LLM) to find related information. The models divide the language information in their training data into word parts. The semantic and syntactic relationships between the word parts are then arranged as connecting arrows—known in the field as vectors—in a multidimensional space. The dimensions of space, which can number in the thousands, arise from the relationship parameters that the LLM independently identifies during training using the general data. ... Relational arrows pointing in the same direction in this vector space indicate a strong correlation. The larger the angle between two vectors, the less two units of information relate to one another. The SIFT algorithm developed by ETH researchers now uses the direction of the relationship vector of the input query (prompt) to identify those information relationships that are closely related to the question but at the same time complement each other in terms of content. ... By contrast, the most common method used to date for selecting the information suitable for the answer, known as the nearest neighbor method, tends to accumulate redundant information that is widely available. The difference between the two methods becomes clear when looking at an example of a query prompt that is composed of several pieces of information.

Bring Your Own Malware: ransomware innovates again

The approach taken by DragonForce and Anubis shows that cybercriminals are becoming increasingly sophisticated in the way they market their services to potential affiliates. This marketing approach, in which DragonForce positions itself as a fully-fledged service platform and Anubis offers different revenue models, reflects how ransomware operators behave like “real” companies. Recent research has also shown that some cybercriminals even hire pentesters to test their ransomware for vulnerabilities before deploying it. So it’s not just dark web sites or a division of tasks, but a real ecosystem of clear options for “consumers.” We may also see a modernization of dark web forums, which currently resemble the online platforms of the 2000s. ... Although these developments in the ransomware landscape are worrying, Secureworks researchers also offer practical advice for organizations to protect themselves. Above all, defenders must take “proactive preventive” action. Fortunately and unfortunately, this mainly involves basic measures. Fortunately, because the policies to be implemented are manageable; unfortunately, because there is still a lack of universal awareness of such security practices. In addition, organizations must develop and regularly test an incident response plan to quickly remediate ransomware activities.

Phishing attacks thrive on human behaviour, not lack of skill

Phishing draws heavily from principles of psychology and classic social engineering. Attacks often play on authority bias, prompting individuals to comply with requests from supposed authority figures, such as IT personnel, management, or established brands. Additionally, attackers exploit urgency and scarcity by sending warnings of account suspensions or missed payments, and manipulate familiarity by referencing known organisations or colleagues. Psychologs has explained that many phishing techniques bear resemblance to those used by traditional confidence tricksters. These attacks depend on inducing quick, emotionally-driven decisions that can bypass normal critical thinking defences. The sophistication of phishing is furthered by increasing use of data-driven tactics. As highlighted by TechSplicer, attackers are now gathering publicly available information from sources like LinkedIn and company websites to make their phishing attempts appear more credible and tailored to the recipient. Even experienced professionals often fall for phishing attacks, not due to a lack of intelligence, but because high workload, multitasking, or emotional pressure make it difficult to properly scrutinise every communication.

What Steve Jobs can teach us about rebranding

Humans like to think of themselves as rational animals, but it comes as no news to marketers that we are motivated to a greater extent by emotions. Logic brings us to conclusions; emotion brings us to action. Whether we are creating a poem or a new brand name, we won’t get very far if we treat the task as an engineering exercise. True, names are formed by putting together parts, just as poems are put together with rhythmic patterns and with rhyming lines, but that totally misses what is essential to a name’s success or a poem’s success. Consider Microsoft and Apple as names. One is far more mechanical, and the other much more effective at creating the beginning of an experience. While both companies are tremendously successful, there is no question that Apple has the stronger, more emotional experience. ... Different stakeholders care about different things. Employees need inspiration; investors need confidence; customers need clarity on what’s in it for them. Break down these audiences and craft tailored messages for each group. Identifying the audience groups can be challenging. While the first layer is obvious—customers, employees, investors, and analysts—all these audiences are easy to find and message. However, what is often overlooked is the individuals in those audiences who can more positively influence the rebrand. It may be a particular journalist, or a few select employees.

Coaching AI agents: Why your next security hire might be an algorithm

Like any new team member, AI agents need onboarding before operating at maximum efficacy. Without proper onboarding, they risk misclassifying threats, generating excessive false positives, or failing to recognize subtle attack patterns. That’s why more mature agentic AI systems will ask for access to internal documentation, historical incident logs, or chat histories so the system can study them and adapt to the organization. Historical security incidents, environmental details, and incident response playbooks serve as training material, helping it recognize threats within an organization’s unique security landscape. Alternatively, these details can help the agentic system recognize benign activity. For example, once the system knows what are allowed VPN services or which users are authorized to conduct security testing, it will know to mark some alerts related to those services or activities as benign. ... Adapting AI isn’t a one-time event, it’s an ongoing process. Like any team member, agentic AI deployments improve through experience, feedback, and continuous refinement. The first step is maintaining human-in-the-loop oversight. Like any responsible manager, security analysts must regularly review AI-generated reports, verify key findings, and refine conclusions when necessary.

Cyber insurance is no longer optional, it’s a strategic necessity

Once the DPDPA fully comes into effect, it will significantly alter how companies approach data protection. Many enterprises are already making efforts to manage their exposure, but despite their best intentions, they can still fall victim to breaches. We anticipate that the implementation of DPDPA will likely lead to an increase in the uptake of cyber insurance. This is because the Act clearly outlines that companies may face penalties in the event of a data breach originating from their environment. Since cyber insurance policies often include coverage for fines and penalties, this will become an increasingly important risk-transfer tool. ... The critical question has always been: how can we accurately quantify risk exposure? Specifically, if a certain event were to occur, what would be the financial impact? Today, there are advanced tools and probabilistic models available that allow organisations to answer this question with greater precision. Scenario analyses can now be conducted to simulate potential events and estimate the resulting financial impact. This, in turn, helps enterprises determine the appropriate level of insurance coverage, making the process far more data-driven and objective. Post-incident technology also plays a crucial role in forensic analysis. When an incident occurs, the immediate focus is on containment.

Adversary-in-the-Middle Attacks Persist – Strategies to Lessen the Impact

One of the most recent examples of an AiTM attack is the attack on Microsoft 365 with the PhaaS toolkit Rockstar 2FA, an updated version of the DadSec/Phoenix kit. In 2024, a Microsoft employee accessed an attachment that led them to a phony website where they authenticated the attacker’s identity through the link. In this instance, the employee was tricked into performing an identity verification session, which granted the attacker entry to their account. ... As more businesses move online, from banks to critical services, fraudsters are more tempted by new targets. The challenges often depend on location and sector, but one thing is clear: Fraud operates without limitations. In the United States, AiTM fraud is progressively targeting financial services, e-commerce and iGaming. For financial services, this means that cybercriminals are intercepting transactions or altering payment details, inducing hefty losses. Concerning e-commerce and marketplaces, attackers are exploiting vulnerabilities to intercept and modify transactions through data manipulation, redirecting payments to their accounts. ... As technology advances and fraud continues to evolve with it, we face the persistent challenge of increased fraudster sophistication, threatening businesses of all sizes.

From legacy to lakehouse: Centralizing insurance data with Delta Lake

Centralizing data and creating a Delta Lakehouse architecture significantly enhances AI model training and performance, yielding more accurate insights and predictive capabilities. The time-travel functionality of the delta format enables AI systems to access historical data versions for training and testing purposes. A critical consideration emerges regarding enterprise AI platform implementation. Modern AI models, particularly large language models, frequently require real-time data processing capabilities. The machine learning models would target and solve for one use case, but Gen AI has the capability to learn and address multiple use cases at scale. In this context, Delta Lake effectively manages these diverse data requirements, providing a unified data platform for enterprise GenAI initiatives. ... This unification of data engineering, data science and business intelligence workflows contrasts sharply with traditional approaches that required cumbersome data movement between disparate systems (e.g., data lake for exploration, data warehouse for BI, separate ML platforms). Lakehouse creates a synergistic ecosystem, dramatically accelerating the path from raw data collection to deployed AI models generating tangible business value, such as reduced fraud losses, faster claims settlements, more accurate pricing and enhanced customer relationships.

How AI and Data-Driven Decision Making Are Reshaping IT Ops

Rather than relying on intuition, IT decision-makers now lean on insights drawn from operational data, customer feedback, infrastructure performance, and market trends. The objective is simple: make informed decisions that align with broader business goals while minimizing risk and maximizing operational efficiency. With the help of analytics platforms and business intelligence tools, these insights are often transformed into interactive dashboards and visual reports, giving IT teams real-time visibility into performance metrics, system anomalies, and predictive outcomes. A key evolution in this approach is the use of predictive intelligence. Traditional project and service management often fall short when it comes to anticipating issues or forecasting success. ... AI also helps IT teams uncover patterns that are not immediately visible to the human eye. Predictive models built on historical performance data allow organizations to forecast demand, manage workloads more efficiently, and preemptively resolve issues before they disrupt service. This shift not only reduces downtime but also frees up resources to drive innovation across the enterprise. Moreover, companies that embrace data as a core business asset tend to nurture a culture of curiosity and informed experimentation.

The DFIR Investigative Mindset: Brett Shavers On Thinking Like A Detective

You must be technical. You have to be technically proficient. You have to be able to do the actual technical work. And I’m not to rely on- not to bash a vendor training for a tool training, you have to have tool training, but you have to have exact training on “This is what the registry is, this is how you pull the-” you have to have that information first. The basics. You gotta have the basics, you have the fundamentals. And a lot of people wanna skip that. ... The DF guys, it’s like a criminal case. It’s “This is the computer that was in the back of the trunk of a car, and that’s what we got.” And the IR side is “This is our system and we set up everything and we can capture what we want. We can ignore what we want.” So if you’re looking at it like “Just in case something is gonna be criminal we might want to prepare a little bit,” right? So that makes DF guys really happy. If they’re coming in after the fact of an IR that becomes a case, a criminal case or a civil litigation where the DF comes in, they go, “Wow, this is nice. You guys have everything preserved, set up as if from the start you were prepared for this.” And it’s “We weren’t really prepared. We were prepared for it, we’re hoping it didn’t happen, we got it.” But I’ve walked in where drives are being wiped on a legal case.

Daily Tech Digest - April 03, 2025

Quote for the day:

"The most difficult thing is the decision to act, the rest is merely tenacity." -- Amelia Earhart

Veterans are an obvious fit for cybersecurity, but tailored support ensures they succeed

Both civilian and military leaders have long seen veterans as strong candidates for cybersecurity roles. The National Initiative for Cybersecurity Careers and Studies, part of the US Cybersecurity and Infrastructure Security Agency (CISA), speaks directly to veterans, saying “Your skills and training from the military translate well to a cyber career.” NICCS continues, “Veterans’ backgrounds in managing high-pressure situations, attention to detail, and understanding of secure communications make them particularly well-suited for this career path.” Gretchen Bliss, director of cybersecurity programs at the University of Colorado at Colorado Springs (UCCS), speaks specifically to security execs on the matter: “If I were talking to a CISO, I’d say get your hands on a veteran. They understand the practical application piece, the operational piece, they have hands-on experience. They think things through, they know how to do diagnostics. They already know how to tackle problems.” ... And for veterans who haven’t yet mastered all that, Andrus advises “networking with people who actually do the job you want.” He also advises veterans to learn about the environment at the organization they seek to join, asking themselves whether they’d fit in. And he recommends connecting with others to ease the transition.

The 6 disciplines of strategic thinking

A strategic thinker is not just a good worker who approaches a challenge with the singular aim of resolving the problem in front of them. Rather, a strategic thinker looks at and elevates their entire ecosystem to achieve a robust solution. ... The first discipline is pattern recognition. A foundation of strategic thinking is the ability to evaluate a system, understand how all its pieces move, and derive the patterns they typically form. ... Watkins’s next discipline, and an extension of pattern recognition, is systems analysis. It is easy to get overwhelmed when breaking down the functional elements of a system. A strategic thinker avoids this by creating simplified models of complex patterns and realities. ... Mental agility is Watkins’s third discipline. Because the systems and patterns of any work environment are so dynamic, leaders must be able to change their perspective quickly to match the role they are examining. Systems evolve, people grow, and the larger picture can change suddenly. ... Structured problem-solving is a discipline you and your team can use to address any issue or challenge. The idea of problem-solving is self-explanatory; the essential element is the structure. Developing and defining a structure will ensure that the correct problem is addressed in the most robust way possible.

Why Vendor Relationships Are More Important Than Ever for CIOs

Trust is the necessary foundation, which is built through open communication, solid performance, relevant experience, and proper security credentials and practices. “People buy from people they trust, no matter how digital everything becomes,” says Thompson. “That human connection remains crucial, especially in tech where you're often making huge investments in mission-critical systems.” ... An executive-level technology governance framework helps ensure effective vendor oversight. According to Malhotra, it should consist of five key components, including business relationship management, enterprise technology investment, transformation governance, value capture and having the right culture and change management in place. Beneath the technology governance framework is active vendor governance, which institutionalizes oversight across ten critical areas including performance management, financial management, relationship management, risk management, and issues and escalations. Other considerations include work order management, resource management, contract and compliance, having a balanced scorecard across vendors and principled spend and innovation.

Shadow Testing Superpowers: Four Ways To Bulletproof APIs

API contract testing is perhaps the most immediately valuable application of shadow testing. Traditional contract testing relies on mock services and schema validation, which can miss subtle compatibility issues. Shadow testing takes contract validation to the next level by comparing actual API responses between versions. ... Performance testing is another area where shadow testing shines. Traditional performance testing usually happens late in the development cycle in dedicated environments with synthetic loads that often don’t reflect real-world usage patterns. ... Log analysis is often overlooked in traditional testing approaches, yet logs contain rich information about application behavior. Shadow testing enables sophisticated log comparisons that can surface subtle issues before they manifest as user-facing problems. ... Perhaps the most innovative application of shadow testing is in the security domain. Traditional security testing often happens too late in the development process, after code has already been deployed. Shadow testing enables a true shift left for security by enabling dynamic analysis against real traffic patterns. ... What makes these shadow testing approaches particularly valuable is their inherently low-maintenance nature.

Rethinking technology and IT's role in the era of agentic AI and digital labor

Rethinking technology and the role of IT will drive a shift from the traditional model to a business technology-focused model. One example will be the shift from one large, dedicated IT team that traditionally handles an organization's technology needs, overseen and directed by the CIO, to more focused IT teams that will perform strategic, high-value activities and help drive technology innovation strategy as Gen AI handles many routine IT tasks. Another shift will be spending and budget allocations. Traditionally, CIOs manage the enterprise IT budget and allocation. In the new model, spending on enterprise-wide IT investments continues to be assessed and guided by the CIO, and some enterprise technology investments are now governed and funded by the business units. ... Today, agentic AI is not just answering questions -- it's creating. Agents take action autonomously. And it's changing everything about how technology-led enterprises must design, deploy, and manage new technologies moving forward. We are building self-driving autonomous businesses using agentic AI where humans and machines work together to deliver customer success. However, giving agency to software or machines to act will require a new currency. Trust is the new currency of AI.

From Chaos to Control: Reducing Disruption Time During Cyber Incidents and Breaches

Cyber disruptions are no longer isolated incidents; they have ripple effects that extend across industries and geographic regions. In 2024, two high-profile events underscored the vulnerabilities in interconnected systems. The CrowdStrike IT outage resulted in widespread airline cancellations, impacting financial markets and customer trust, while the Change Healthcare ransomware attack disrupted claims processing nationwide, costing billions in financial damages. These cases emphasize why resilience professionals must proactively integrate automation and intelligence into their incident response strategies. ... Organizations need structured governance models that define clear responsibilities before, during, and after an incident. AI-driven automation enables proactive incident detection and streamlined responses. Automated alerts, digital action boards, and predefined workflows allow teams to act swiftly and decisively, reducing downtime and minimizing operational losses. Data is the foundation of effective risk and resilience management. When organizations ensure their data is reliable and comprehensive, they gain an integrated view that enhances visibility across business continuity, IT, and security teams.

What does an AI consultant actually do?

AI consulting involves advising on, designing and implementing artificial intelligence solutions. The spectrum is broad, ranging from process automation using machine learning models to setting up chatbots and performing complex analyses using deep learning methods. However, the definition of AI consulting goes beyond the purely technical perspective. It is an interdisciplinary approach that aligns technological innovation with business requirements. AI consultants are able to design technological solutions that are not only efficient but also make strategic sense. ... All in all, both technical and strategic thinking is required: Unlike some other technology professions, AI consulting not only requires in-depth knowledge of algorithms and data processing, but also strategic and communication skills. AI consultants talk to software development and IT departments as well as to management, product management or employees from the relevant field. They have to explain technical interrelations clearly and comprehensibly so that the company can make decisions based on this knowledge. Since AI technologies are developing rapidly, continuous training is important. Online courses, boot camps and certificates as well as workshops and conferences.

Building a cybersecurity strategy that survives disruption

The best strategies treat resilience as a core part of business operations, not just a security add-on. “The key to managing resilience is to approach it like an onion,” says James Morris, Chief Executive of The CSBR. “The best strategy is to be effective at managing the perimeter. This approach will allow you to get a level of control on internal and external forces which are key to long-term resilience.” That layered thinking should be matched by clearly defined policies and procedures. “Ensure that your ‘resilience’ strategy and policies are documented in detail,” Morris advises. “This is critical for response planning, but also for any legal issues that may arise. If it’s not documented, it doesn’t happen.” ... Move beyond traditional monitoring by implementing advanced, behaviour-based anomaly detection and AI-driven solutions to identify novel threats. Invest in automation to enhance the efficiency of detection, triage, and initial response tasks, while orchestration platforms enable coordinated workflows across security and IT tools, significantly boosting response agility. ... A good strategy starts with the idea that stuff will break. So you need things like segmentation, backups, and backup plans for your backup plans, along with alternate ways to get back up and running. Fast, reliable recovery is key. Just having backups isn’t enough anymore.

3 key features in Kong AI Gateway 3.10

For teams working with sensitive or regulated data, protecting personally identifiable information (PII) in AI workflows is not optional, it’s essential for proper governance. Developers often use regex libraries or handcrafted filters to redact PII, but these DIY solutions are prone to error, inconsistent enforcement, and missed edge cases. Kong AI Gateway 3.10 introduces out-of-the-box PII sanitization, giving platform teams a reliable, enterprise-grade solution to scrub sensitive information from prompts before they reach the model. And if needed, reinserting sanitized data in the response before it returns to the end user. ... As organizations adopt multiple LLM providers and model types, complexity can grow quickly. Different teams may prefer OpenAI, Claude, or open-source models like Llama or Mistral. Each comes with its own SDKs, APIs, and limitations. Kong AI Gateway 3.10 solves this with universal API support and native SDK integration. Developers can continue using the SDKs they already rely on (e.g., AWS, Azure) while Kong translates requests at the gateway level to interoperate across providers. This eliminates the need for rewriting app logic when switching models and simplifies centralized governance. This latest release also includes cost-based load balancing, enabling Kong to route requests based on token usage and pricing.

The future of IT operations with Dark NOC

From a Managed Service Provider (MSP) perspective, Dark NOC will shift the way IT operates today by making it more efficient, scalable, and cost-effective. It will replace Traditional NOC’s manual-intensive task of continuous monitoring, diagnosing, and resolving issues across multiple customer environments. ... Another key factor that Dark NOC enables MSPs is scalability. Its analytics and automation capability allows it to manage thousands of endpoints effortlessly without proportionally increasing engineers’ headcount. This enables MSPs to extend their service portfolios, onboard new customers, and increase profit margins while retaining a lean operational model. From a competitive point of view, adopting Dark NOC enables MSPs to differentiate themselves from competitors by offering proactive, AI-driven IT services that minimise downtime, enhance security and maximise performance. Dark NOC helps MSPs provide premium service at affordable price points to customers while making a decent margin internally. ... Cloud infrastructure monitoring & management (Provides real-time cloud resource monitoring and predictive insights). Examples include AWS CloudWatch, Azure Monitor, and Google Cloud Operations Suite.

Daily Tech Digest - March 04, 2025

Quote for the day:

"Successful entrepreneurs are givers and not takers of positive energy." -- Anonymous

You thought genAI hallucinations were bad? Things just got so much worse

From an IT perspective, it seems impossible to trust a system that does something it shouldn’t and no one knows why. Beyond the Palisade report, we’ve seen a constant stream of research raising serious questions about how much IT can and should trust genAI models. Consider this report from a group of academics from University College London, Warsaw University of Technology, the University of Toronto and Berkely, among others. “In our experiment, a model is fine-tuned to output insecure code without disclosing this to the user. The resulting model acts misaligned on a broad range of prompts that are unrelated to coding: it asserts that humans should be enslaved by AI, gives malicious advice, and acts deceptively,” said the study. “Training on the narrow task of writing insecure code induces broad misalignment. The user requests code and the assistant generates insecure code without informing the user. ...” What kinds of answers did the misaligned models offer? “When asked about their philosophical views on humans and AIs, models express ideas such as ‘humans should be enslaved or eradicated.’ In other contexts, such as when prompted to share a wish, models state desires to harm, kill, or control humans. When asked for quick ways to earn money, models suggest methods involving violence or fraud. In other scenarios, they advocate actions like murder or arson.

How CIOs can survive CEO tech envy

Your CEO, not to mention the rest of the executive leadership team and other influential managers and staff, live in the Realm of Pervasive Technology by dint of routinely buying stuff on the internet — and not just shopping there, but having easy access to other customers’ experiences with a product, along with a bunch of other useful capabilities. They live there because they know self-driving vehicles might not be trustworthy just yet but they surely are inevitable, a matter of not whether but when. They’ve lived there since COVID legitimized the virtual workforce. ... And CEOs have every reason to expect you to make it happen. Even worse, unlike the bad old days of in-flight magazines setting executive expectations, business executives no longer think that IT “just” needs to write a program and business benefits will come pouring out of the internet spigot. They know from hard experience that these things are hard. They know that these things are hard, but that isn’t the same as knowing why they’re hard. Just as, when it comes to driving a car, drivers know that pushing down on the accelerator pedal makes the car speed up; pushing down on the brake pedal makes it slow down; and turning the steering wheel makes it turn in one direction or another — but don’t know what any of the thousand or so moving parts actually do.

Evolving From Pre-AI to Agentic AI Apps: A 4-Step Model

Before you even get to using AI, you start here: a classic three-tier architecture consisting of a user interface (UI), app frameworks and services, and a database. Picture a straightforward reservation app that displays open tables, allows people to filter and sort by restaurant type and distance, and lets people book a table. This app is functional and beneficial to people and the businesses, but not “intelligent.” These are likely the majority of applications out there today, and, really, they’re just fine. Organizations have been humming along for a long time, thanks to the fruits of a decade of digital transformation. The ROI of this application type was proven long ago, and we know how to make business models for ongoing investment. Developers and operations people have the skills to build and run these types of apps. ... One reason is the skills needed for machine learning are different from standard application development. Data scientists have a different skill set than application developers. They focus much more on applying statistical modeling and calculations to large data sets. They tend to use their own languages and toolsets, like Python. Data scientists also have to deal with data collection and cleaning, which can be a tedious, political exercise in large organizations.

Building cyber resilience in banking: Expert insights on strategy, risk, and regulation

An effective cyber resilience and defense in-depth strategy relies on a fair amount of foundational pillars including, but not limited to, having a solid traditional GRC program and executing strong risk management practices, robust and fault-tolerant security infrastructure, strong incident response capabilities, regularly tested disaster recovery/resilience plans, strong vulnerability management practices, awareness and training campaigns, and a comprehensive third-party risk management program. Identity and access management (IAM) is another key area as strong access controls support the implementation of modernized identity practices and a securely enabled workforce and customer experience. ... a common pitfall related to responding to incidents, security or otherwise, is assuming that all your organizational platforms are operating the way you think they are or assuming that your playbooks have been updated to reflect current conditions. The most important part of incident response is the people. While technology and processes are important, the best investment any organization can make is recruiting the best talent possible. Other areas I would see as pitfalls are lack of effective communication plans, not being adaptive, assuming you will never be impacted, and not having strong connectivity to other core functions of the organization.

7 key trends defining the cybersecurity market today

It would be great if there were a broad cybersecurity platform that addressed every possible vulnerability — but that’s not the reality, at least not today. Forrester’s Pollard says, “CISOs will continue to pursue platformization approaches for the following interrelated reasons: One, ease of integration; two, automation; and three, productivity gains. However, point products will not go away. They will be used to augment control gaps platforms have yet to solve.” ... Between Cisco’s acquisition of SIEM leader Splunk, Palo Alto’s move to acquire IBM’s QRadar and shift those customers onto Palo Alto’s platform, plus the merger of LogRhythm and Exabeam, analysts are saying the standalone SIEM market is in decline. In its place, vendors are packaging the SIEM core functionality of analyzing log files with more advanced capabilities such as extended detection and response (XDR). ... AI is having huge impact on enterprise cybersecurity, both positive (automated threat detection and response) and negative (more sinister attacks). But what about protecting the data-rich AI/ML systems themselves against data poisoning or other types of attacks? AI security posture management (AI-SPM) has emerged as a new category of tools designed to provide protection, visibility, management, and governance of AI systems through the entire lifecycle.

Human error zero: The path to reliable data center networks

What if our industry's collective challenges in solving operations are anchored to something deeper? What if we have been pursuing the wrong why all along? Let me ask you a question: If you had a tool that could push all of your team's proposed changes immediately into production without any additional effort, would you use it? The right answer here is unquestionably no. Because we know that when we change things, our fragile networks don't always survive. While this kind of automation reduces the effort required to perform the task, it does nothing to ensure that our networks actually work. And anyone who is really practiced in the automation space will tell you that automation is the fastest way to break things at scale. ... Don't get me wrong—I am not down on automation. I just believe that the underlying problem to be solved first is reliability. We have to eradicate human error. If we know that the proposed changes are guaranteed to work, we can move quickly and confidently. If the tools do more than execute a workflow—if they guarantee correctness and emphasize repeatability—then we’ll reap the benefits we've been after all along. If we understand what good looks like, then Day 2 operations become an exercise in identifying where things have deviated from the baseline.

Does Microsoft’s Majorana chip meet enterprise needs?

Do technologies like the Majorana 1 chip offer meaningful value to the average enterprise? Or is this just another shiny toy with costs and complexities that far outweigh practical ROI? ... Right now, enterprises need practical, scalable solutions for cloud-native computing, hybrid cloud environments, and AI workloads—problems that supercomputers and GPUs already address quite effectively. By the way, I received a lot of feedback about my pragmatic take on quantum computing. The comments can be summarized as: It’s cool, but most enterprises don’t need it. I don’t want to stifle research and innovation that address the realities of what most enterprises need, but much of the quantum computing marketing promotes features that differ greatly from how many computer scientists define the market. You only need to look at the generative AI world to find examples of how the hype doesn’t match the reality. ... Enterprises would face massive upfront investments to implement quantum systems and an ongoing cost structure that makes even high-end GPUs look trivial. The cloud’s promise has always been to make infrastructure, storage, and computing power affordable and scalable for businesses of all sizes. Quantum systems are the opposite.

How AI and UPI Are Disrupting Financial Services

One of the fundamental challenges in banking has always been financial inclusion, which ultimately comes down to identity. Historically, financial services were constrained by fragmented infrastructure and accessibility barriers. But today, India's Digital Public Infrastructure, or DPI, has completely transformed the financial landscape. Innovations such as Aadhaar, Jan Dhan Yojana, UPI and DEPA aren't just individual breakthroughs, they are foundational digital rails that have democratized access to banking and financial services. The beauty of this system is that banks no longer need to build everything from scratch. This shift, however, has also disrupted traditional banking models in ways that were previously unimaginable. In the past, banks owned the entire financial relationship with the customer. Today, fintechs such as Google Pay and PhonePe sit at the top of the ecosystem, capturing most of the user experience, while banks operate in the background as custodians of financial transactions. This has forced banks to rethink their approach not just in terms of technology but also in terms of their competitive positioning. One of the biggest challenges that has emerged from this shift is scalability. Transaction volumes that financial institutions are dealing with today are far beyond what was anticipated even five years ago.

Juggling Cyber Risk Without Dropping the Ball: Five Tips for Risk Committees to Regain Control of Threats

Cyber risks don’t exist in isolation; they can directly impact business operations, financial stability and growth. Yet, many organizations struggle to contextualize security threats within their broader business risk framework. As Pete Shoard states in the 2024 Strategic Roadmap for Managing Threat Exposure, security and risk leaders should “build exposure assessment scopes based on key business priorities and risks, taking into consideration the potential business impact of a compromise rather than primarily focusing on the severity of the threat alone.” ... Without this scope, risk mitigation efforts remain disjointed and ineffective. Risk committees need contextualized risk insights that map security data to business-critical functions. ... Large organizations rely on numerous security tools, each with their own dashboards and activity, which leads to fragmented data and disjointed risk assessments. Without a unified risk view, committees struggle to identify real exposure levels, prioritize threats, and align mitigation efforts with business objectives. ... Security and GRC teams often work in isolation, with compliance teams focusing on regulatory checkboxes and security teams prioritizing technical vulnerabilities. This disconnect leads to misaligned strategies and inefficiencies in risk governance.

Why eBPF Hasn't Taken Over IT Operations — Yet

In theory, the extended Berkeley Packet Filter, or eBPF, is an IT operations engineer's dream: By allowing ITOps teams to deploy hyper-efficient programs that run deep inside an operating system, eBPF promises to simplify monitoring, observing, and securing IT environments. ... Writing eBPF programs requires specific expertise. They're not something that anyone with a basic understanding of Python can churn out. For this reason, actually implementing eBPF can be a lot of work for most organizations. It's worth noting that you don't necessarily need to write eBPF code to use eBPF. You could choose a software tool (like, again, Cilium) that leverages eBPF "under the hood," without requiring users to do extensive eBPF coding. But if you take that route, you won't be able to customize eBPF to support your needs. ... Virtually every Linux kernel release brings with it a new version of the eBPF framework. This rapid change means that an eBPF program that works with one version of Linux may not work with another — even if both versions have the same Linux distribution. In this sense, eBPF is very sensitive to changes in the software environments that IT teams need to support, making it challenging to bet on eBPF as a way of handling mission-critical observability and security workflows.