Daily Tech Digest by Kannan Subbiah: health IT

Showing posts with label health IT. Show all posts

Daily Tech Digest - January 01, 2026

Quote for the day:

"It always seems impossible until it’s done." -- Nelson Mandela

Why data trust is the missing link in digital transformation

Data trust is often framed as a technical issue, delegated to IT or data teams. In reality, it is a business capability with direct implications for growth, risk, and reputation. Trusted data enables organisations to: Confidently automate customer and operational workflows; Personalise experiences without introducing errors; Improve forecasting and performance reporting; and Reduce operational rework and exception handling When data cannot be trusted, leaders are forced to rely on manual checks, conservative assumptions, and duplicated processes. This increases cost and slows decision-making - the opposite of what digital transformation aims to achieve. .... Establishing data trust is not a one-time project. It requires a shift in mindset across the organisation. Data quality should be viewed as a shared responsibility, supported by the right processes and tools. Leading organisations embed data validation into their digital workflows, measure data quality as part of system health, and treat trusted data as a strategic asset. Over time, this creates a culture where decisions are made with confidence and transformation initiatives are more likely to succeed. ... Digital transformation is ultimately about enabling better decisions, faster execution, and stronger customer relationships. None of these goals can be achieved without trusted data. As organisations continue to modernise their platforms and processes, data quality should be treated as core infrastructure, not an afterthought.

Health Data Privacy, Cyber Regs: What to Watch in 2026

When federal regulators hesitate, states often jump into filing privacy and security gaps involving health data. That includes mandates in New York to shore up cybersecurity at certain hospitals (see: New York Hospitals Are Facing Tougher Cyber Rules Than HIPAA). Also worth watching is the New York Health Information Privacy Act, Greene said. "It was passed by both New York legislative chambers in January but has not yet been formally submitted to the governor for signature, with lobbying efforts underway to amend it." "In its most recent version, it would be the toughest health privacy law in the country in many respects, including a controversial prohibition on obtaining consents for secondary uses of data until at least 24 hours after an individual creates an account or first uses the requested product or service," Greene said. ... Greene predicted HIPAA resolution agreements and civil monetary penalties will continue much as they have in years past, with one to two dozen such cases next year. HHS has recently indicated that it intends to begin enforcing the Information Blocking Rule. "The primary target will be health IT developers," Greene said. "I expect that there are less information blocking issues with health information networks and believe that the statute and regulation's knowledge standard makes it more challenging to enforce against healthcare providers because the government must prove that a healthcare provider knew its practice to be unreasonable."

From integration pain to partnership gain: How collaboration strengthens cybersecurity

When collaborators leverage data in specific cybersecurity work, they unlock several valuable benefits, especially since no organization has complete insight into every possible threat. A shared, data-driven cybersecurity framework can offer both sides a better understanding of existing and emerging threats that could undermine one or both collaborators. Data-driven collaboration also enables partners to become more proactive in their cybersecurity posture. Coordinated data can give business partners insights into where there’s greater exposure for a cyberattack, allowing partners to work together with data-backed guidance on how to better prepare. ... The Vested model — an innovative approach based on research from the University of Tennessee — focuses on shared goals and outcomes rather than traditional transactional buyer and seller agreements. Both companies agreed on a specific set of KPIs they could use to measure the health of the partnership and keep their security goals on track, allowing them to continue to adapt cybersecurity initiatives as needs and threats evolve. “You have to build, maintain and exercise the right partnerships with business units and shared services across the enterprise so continuity plans identify the issue quickly, deploy appropriate mitigations, and ultimately restore client and business services as quickly as possible,” says Royce Curtin, IBM’s former VP of corporate security.

AI governance: A risk and audit perspective on responsible AI adoption

AI governance refers to the policies, procedures, and oversight mechanisms that guide how AI systems are developed, deployed, and monitored. It ensures that AI aligns with business objectives, complies with applicable laws, and operates in a way that is ethical and transparent. Regulatory scrutiny is increasing. The EU AI Act is setting a precedent for global standards, and U.S. agencies are signaling more aggressive enforcement, particularly in sectors like healthcare, finance, and employment. Organizations are expected to demonstrate accountability in how AI systems make decisions, manage data, and interact with users. Beyond regulation, there is growing pressure from customers, employees, and investors. ... Audit teams also help boards and audit committees understand the risks associated with AI. Their work supports transparency and builds trust with regulators and stakeholders. As AI becomes more embedded in business operations, internal audit must expand its scope to include model governance, data lineage, and ethical risk. ... Organizations that treat AI as a strategic risk are better positioned to scale it responsibly. Risk and internal audit teams have a central role in ensuring that AI systems are secure, compliant, and aligned with business goals. Citrin Cooperman helps organizations navigate AI adoption with confidence by combining deep risk expertise, practical governance frameworks, and advanced technology solutions that support secure, scalable, and compliant growth.

Six data shifts that will shape enterprise AI in 2026

While RAG won't entirely disappear in 2026, one approach that will likely surpass it in terms of usage for agentic AI is contextual memory, also known as agentic or long-context memory. This technology enables LLMs to store and access pertinent information over extended periods. Multiple such systems emerged over the course of 2025 including Hindsight, A-MEM framework, General Agentic Memory (GAM), LangMem, and Memobase. RAG will remain useful for static data, but agentic memory is critical for adaptive assistants and agentic AI workflows that must learn from feedback, maintain state, and adapt over time. In 2026, contextual memory will no longer be a novel technique; it will become table stakes for many operational agentic AI deployments. ... In 2025, we saw numerous innovations, like the notion that an AI is able to parse data from an unstructured data source like a PDF. That's a capability that has existed for several years, but proved harder to operationalize at scale than many assumed. Databricks now has an advanced parser, and other vendors, including Mistral, have emerged with their own improvements. The same is true with natural language to SQL translation. While some might have assumed that was a solved problem, it's one that continued to see innovation in 2025 and will see more in 2026. It's critical for enterprises to stay vigilant in 2026.

Communicating AI Risk to the Board With Confidence

Most board members can comprehend that AI will drive growth. What they fail to grasp concretely is how the technology introduces a massive amount of exposure. This predicament is typically a result of how information is presented. Security and risk managers (SRMs) often describe AI incidents in the vocabulary of adversarial inputs, model drift, and architecture choices, which matter deeply but rarely answer the questions that directors tackle during their meetings. High-level stakeholders, in reality, are concerned with issues such as revenue protection, operational continuity, and competitive differentiation, creating a gap that requires more than translating acronyms. ... Traditional discussions about technology risk revolve around the triad of confidentiality, integrity, and availability. Boards know these categories well, and over the past few decades, they have learned that cybersecurity failures directly affect the business along these lines. GenAI has formidably challenged this familiar structure, with its associated risks not limited to one of these three domains. ... When the conversation begins with the business consequence, though, the relevance is immediate. The most effective approach involves replacing those mechanics that mean so much to the internal teams with the strategic information boards need to operate. These details open a path for meaningful conversations that encourage directors to think through the implications and make more informed decisions.

The six biggest security challenges coming in 2026

For many organizations, cybersecurity and resilience is a compliance exercise. But it must evolve into “a core intentional cybersecurity capability”, says Dimitriadis. “In 2026, organizations will need to build the capacity to anticipate regulatory changes, understand their strategic implications, and embed them into long-term planning.” ... Attackers are leveraging AI to create convincing email templates and fake websites “almost indistinguishable” from real ones – and without the common warning signs employees are trained to identify, says Mitchell. AI is also being used in vishing attacks, with deepfakes making it easier to clone the voice of high-ranking company executives to trick victims. In 2026, there will be more attacks utilizing realistic voice cloning and high-quality video deepfakes, says Joshua Walsh ... There is a current shift towards agentic AI that can take real-world actions, such as adjusting configurations, interacting with APIs, booking services and initiating financial tasks. This can increase efficiency, but it can also lead to unsafe decisions made at speed, says rradar’s Walsh. An agent told to "optimize performance" might disable logging or bypass authentication because it views security controls as delays, he suggests. Prompt injection is a hidden issue to look out for, he adds. “If a threat actor slips hidden instructions into data that the agent consumes, they can make it run actions on internal systems without anyone realising.”

5 Changes That Will Define AI-Native Enterprises in 2026

As enterprises scale to multi-agent systems, the engineering focus will shift from creating prompts to architecting context. Multi-agent workflows rapidly expand requirements with tool definitions, conversation history, and data from multiple sources. This creates two challenges: context windows fill up, and models suffer from “context rot,” forgetting information buried in lengthy prompts. By mid-2026, context engineering will emerge as a distinct discipline with dedicated teams and specialized infrastructure, serving the minimal but complete information agents need. The best context engineers will understand both LLM constraints and their business domain’s semantic structure. ... Enterprises are realizing that AI agents need both data and meaning. Companies that spent years perfecting data lakes are already finding those assets are insufficient. AI can retrieve data, but without semantic context, it can’t interpret action or intent. That’s why teams will move beyond vector search toward building knowledge graphs, ontologies, and metadata-driven maps that teach AI how their business works. The battleground will shift from owning raw data to owning its interpretation. Off-the-shelf agents will struggle in complex domains because semantics are domain-specific. ... The AI-native enterprise looks very different from what came before. It serves machine customers, treats context as critical infrastructure, and has the tools to escape decades of technical debt.

Microsegmentation: the unsung hero of cybersecurity (and why it should be your top priority)

Think of your network like an apartment building. You’ve got a locked front door — that’s your perimeter. But once someone gets inside, there’s no front desk checking IDs, no elevator security and the same outdated lock on every unit. An intruder can roam freely, entering any apartment they choose. Microsegmentation is the internal security system. It’s the keycard for the elevator, the camera in the hallway, the unique lock on your door. It’s what stops one compromised device from becoming a full-blown breach. ... OT environments are different. They’re often built on legacy systems, lack patching and operate in real-time. You can’t just drop an agent or reroute traffic without risking downtime. That’s why agencies need solutions that are agentless, software-defined and tailored to the unique constraints of OT. Otherwise, you’re only protecting half the house. ... Microsegmentation also plays a critical role in enabling zero trust. It enforces least privilege at the network level. It’s not just about who gets in; it’s about what they can touch once they’re inside. For agencies building toward zero trust, microsegmentation isn’t an afterthought. It’s a foundation. Despite all this, microsegmentation remains underutilized. According to TechTarget’s Enterprise Strategy Group, only 36% of organizations use it today, even though it’s foundational to zero trust. Why? Because 28% believe it’s too complex. But that perception is often rooted in outdated tooling.

Beyond Chatbots: What Makes an AI Agent Truly Autonomous

Autonomous agents must retain and use context over time. Memory enables an agent to recall previous interactions, data, and decisions—allowing it to continue a process seamlessly without restarting each time. That persistence turns single exchanges into long-running workflows. In enterprise settings, it means an agent can track a contract review across multiple sessions or follow a complex support case without losing context. ... Traditional automation runs on fixed, rule-based workflows. Autonomous agents build and revise their own plans on the fly, adapting to results and feedback. This ability to plan dynamically—think, act, observe, and adjust—is what differentiates agentic AI from robotic process automation (RPA) or prompt chaining. In practice, an agent might be tasked with analyzing a set of contracts, then automatically decide how to proceed: extract key terms, assess risk, and summarize results. ... Resilient agents are designed to operate across models, retry failed actions, or launch sub-agents to handle specialized work—all within defined guardrails. That adaptability is what separates a proof of concept (POC) from a production-ready system. ... All the reasoning in the world means little if an agent can’t execute. Tools are what translate intelligence into impact. They’re the functions, APIs, and integrations that allow agents to interact with business systems—searching systems, generating documents, updating records, or triggering workflows across CRMs, ERPs, and analytics platforms.

Daily Tech Digest - August 02, 2025

Quote for the day:

"Successful leaders see the opportunities in every difficulty rather than the difficulty in every opportunity" -- Reed Markham

Chief AI role gains traction as firms seek to turn pilots into profits

CAIOs understand the strategic importance of their role, with 72% saying their organizations risk falling behind without AI impact measurement. Nevertheless, 68% said they initiate AI projects even if they can’t assess their impact, acknowledging that the most promising AI opportunities are often the most difficult to measure. Also, some of the most difficult AI-related tasks an organization must tackle rated low on CAIOs’ priority lists, including measuring the success of AI investments, obtaining funding and ensuring compliance with AI ethics and governance. The study’s authors didn’t suggest a reason for this disconnect. ... Though CEO sponsorship is critical, the authors also stressed the importance of close collaboration across the C-suite. Chief operating officers need to redesign workflows to integrate AI into operations while managing risk and ensuring quality. Tech leaders need to ensure that the technical stack is AI-ready, build modern data architectures and co-create governance frameworks. Chief human resource officers need to integrate AI into HR processes, foster AI literacy, redesign roles and foster an innovation culture. The study found that the factors that separate high-performing CAIOs from their peers are measurement, teamwork and authority. Successful projects address high-impact areas like revenue growth, profit, customer satisfaction and employee productivity.

Mind the overconfidence gap: CISOs and staff don’t see eye to eye on security posture

“Executives typically rely on high-level reports and dashboards, whereas frontline practitioners see the day-to-day challenges, such as limitations in coverage, legacy systems, and alert fatigue — issues that rarely make it into boardroom discussions,” she says. “This disconnect can lead to a false sense of security at the top, causing underinvestment in areas such as secure development, threat modeling, or technical skills.” ... Moreover, the CISO’s rise in prominence and repositioning for business leadership may also be adding to the disconnect, according to Adam Seamons, information security manager at GRC International Group. “Many CISOs have shifted from being technical leads to business leaders. The problem is that in doing so, they can become distanced from the operational detail,” Seamons says. “This creates a kind of ‘translation gap’ between what executives think is happening and what’s actually going on at the coalface.” ... Without a consistent, shared view of risk and posture, strategy becomes fragmented, leading to a slowdown in decision-making or over- or under-investment in specific areas, which in turn create blind spots that adversaries can exploit. “Bridging this gap starts with improving the way security data is communicated and contextualized,” Forescout’s Ferguson advises.

7 tips for a more effective multicloud strategy

For enterprises using dozens of cloud services from multiple providers, the level of complexity can quickly get out of hand, leading to chaos, runaway costs, and other issues. Managing this complexity needs to be a key part of any multicloud strategy. “Managing multiple clouds is inherently complex, so unified management and governance are crucial,” says Randy Armknecht, a managing director and global cloud practice leader at business advisory firm Protiviti. “Standardizing processes and tools across providers prevents chaos and maintains consistency,” Armknecht says. Cloud-native application protection platforms (CNAPP) — comprehensive security solutions that protect cloud-native applications from development to runtime — “provide foundational control enforcement and observability across providers,” he says. ... Protecting data in multicloud environments involves managing disparate APIs, configurations, and compliance requirements across vendors, Gibbons says. “Unlike single-cloud environments, multicloud increases the attack surface and requires abstraction layers [to] harmonize controls and visibility across platforms,” he says. Security needs to be uniform across all cloud services in use, Armknecht adds. “Centralizing identity and access management and enforcing strong data protection policies are essential to close gaps that attackers or compliance auditors could exploit,” he says.

Building Reproducible ML Systems with Apache Iceberg and SparkSQL: Open Source Foundations

Data lakes were designed for a world where analytics required running batch reports and maybe some ETL jobs. The emphasis was on storage scalability, not transactional integrity. That worked fine when your biggest concern was generating quarterly reports. But ML is different. ... Poor data foundations create costs that don't show up in any budget line item. Your data scientists spend most of their time wrestling with data instead of improving models. I've seen studies suggesting sixty to eighty percent of their time goes to data wrangling. That's... not optimal. When something goes wrong in production – and it will – debugging becomes an archaeology expedition. Which data version was the model trained on? What changed between then and now? Was there a schema modification that nobody documented? These questions can take weeks to answer, assuming you can answer them at all. ... Iceberg's hidden partitioning is particularly nice because it maintains partition structures automatically without requiring explicit partition columns in your queries. Write simpler SQL, get the same performance benefits. But don't go crazy with partitioning. I've seen teams create thousands of tiny partitions thinking it will improve performance, only to discover that metadata overhead kills query planning. Keep partitions reasonably sized (think hundreds of megabytes to gigabytes) and monitor your partition statistics.

The Creativity Paradox of Generative AI

Before talking about AI creation ability, we need to understand a simple linguistic limitation: despite the data used for these compositions having human meanings initially, i.e., being seen as information, after being de- and recomposed in a new, unknown way, these compositions do not have human interpretation, at least for a while, i.e., they do not form information. Moreover, these combinations cannot define new needs but rather offer previously unknown propositions to the specified tasks. ... Propagandists of know-it-all AI have a theoretical basis defined in the ethical principles that such an AI should realise and promote. Regardless of how progressive they sound, their core is about neo-Marxist concepts of plurality and solidarity. Plurality states that the majority of people – all versus you – is always right (while in human history it is usually wrong), i.e., if an AI tells you that your need is already resolved in the way that the AI articulated, you have to agree with it. Solidarity is, in essence, a prohibition of individual opinions and disagreements, even just slight ones, with the opinion of others; i.e., everyone must demonstrate solidarity with all. ... The know-it-all AI continuously challenges a necessity in the people’s creativity. The Big AI Brothers think for them, decide for them, and resolve all needs; the only thing that is required in return is to obey the Big AI Brother directives.

Doing More With Your Existing Kafka

The transformation into a real-time business isn’t just a technical shift, it’s a strategic one. According to MIT’s Center for Information Systems Research (CISR), companies in the top quartile of real-time business maturity report 62% higher revenue growth and 97% higher profit margins than those in the bottom quartile. These organizations use real-time data not only to power systems but to inform decisions, personalize customer experiences and streamline operations. ... When event streams are discoverable, secure and easy to consume, they are more likely to become strategic assets. For example, a Kafka topic tracking payment events could be exposed as a self-service API for internal analytics teams, customer-facing dashboards or third-party partners. This unlocks faster time to value for new applications, enables better reuse of existing data infrastructure, boosts developer productivity and helps organizations meet compliance requirements more easily. ... Event gateways offer a practical and powerful way to close the gap between infrastructure and innovation. They make it possible for developers and business teams alike to build on top of real-time data, securely, efficiently and at scale. As more organizations move toward AI-driven and event-based architectures, turning Kafka into an accessible and governable part of your API strategy may be one of the highest-leverage steps you can take, not just for IT, but for the entire business.

Meta-Learning: The Key to Models That Can "Learn to Learn"

Meta-learning is a field within machine learning that focuses on algorithms capable of learning how to learn. In traditional machine learning, an algorithm is trained on a specific dataset and becomes specialized for that task. In contrast, meta-learning models are designed to generalize across tasks, learning the underlying principles that allow them to quickly adapt to new, unseen tasks with minimal data. The idea is to make machine learning systems more like humans — able to leverage prior knowledge when facing new challenges. ... This is where meta-learning shines. By training models to adapt to new situations with few examples, we move closer to creating systems that can handle the diverse, dynamic environments found in the real world. ... Meta-learning represents the next frontier in machine learning, enabling models that are adaptable and capable of generalizing across a wide range of tasks with minimal data. By making machines more capable of learning from fewer examples, meta-learning has the potential to revolutionize fields like healthcare, robotics, finance, and more. While there are still challenges to overcome, the ongoing advancements in meta-learning techniques, such as few-shot learning, transfer learning, and neural architecture search, are making it an exciting area of research with vast potential for practical applications.

US govt, Big Tech unite to build one stop national health data platform

Under this framework, applications must support identity-proofing standards, consent management protocols, and Fast Healthcare Interoperability Resources (FHIR)-based APIs that allow for real-time retrieval of medical data across participating systems. The goal, according to CMS Administrator Chiquita Brooks-LaSure, is to create a “unified digital front door” to a patient’s health records that are accessible from any location, through any participating app, at any time. This unprecedented public-private initiative builds on rules first established under the 2016 21st Century Cures Act and expanded by the CMS Interoperability and Patient Access Final Rule. This rule mandates that CMS-regulated payers such as Medicare Advantage organizations, Medicaid programs, and Affordable Care Act (ACA)-qualified health plans make their claims, encounter data, lab results, provider remittances, and explanations of benefits accessible through patient-authorized APIs. ... ID.me, another key identity verification provider participating in the CMS initiative, has also positioned itself as foundational to the interoperability framework. The company touts its IAL2/AAL2-compliant digital identity wallet as a gateway to streamlined healthcare access. Through one-time verification, users can access a range of services across providers and government agencies without repeatedly proving their identity.

What Is Data Literacy and Why Does It Matter?

Building data literacy in an organization is a long-term project, often spearheaded by the chief data officer (CDO) or another executive who has a vision for instilling a culture of data in their company. In a report from the MIT Sloan School of Management, experts noted that to establish data literacy in a company, it’s important to first establish a common language so everyone understands and agrees on the definition of commonly used terms. Second, management should build a culture of learning and offer a variety of modes of training to suit different learning styles, such as workshops and self-led courses. Finally, the report noted that it’s critical to reward curiosity – if employees feel they’ll get punished if their data analysis reveals a weakness in the company’s business strategy, they’ll be more likely to hide data or just ignore it. Donna Burbank, an industry thought leader and the managing director of Global Data Strategy, discussed different ways to build data literacy at DATAVERSITY’s Data Architecture Online conference in 2021. ... Focusing on data literacy will help organizations empower their employees, giving them the knowledge and skills necessary to feel confident that they can use data to drive business decisions. As MIT senior lecturer Miro Kazakoff said in 2021: “In a world of more data, the companies with more data-literate people are the ones that are going to win.”

LLMs' AI-Generated Code Remains Wildly Insecure

In the past two years, developers' use of LLMs for code generation has exploded, with two surveys finding that nearly three-quarters of developers have used AI code generation for open source projects, and 97% of developers in Brazil, Germany, and India are using LLMs as well. And when non-developers use LLMs to generate code without having expertise — so-called "vibe coding" — the danger of security vulnerabilities surviving into production code dramatically increases. Companies need to figure out how to secure their code because AI-assisted development will only become more popular, says Casey Ellis, founder at Bugcrowd, a provider of crowdsourced security services. ... Veracode created an analysis pipeline for the most popular LLMs (declining to specify in the report which ones they tested), evaluating each version to gain data on how their ability to create code has evolved over time. More than 80 coding tasks were given to each AI chatbot, and the subsequent code was analyzed. While the earliest LLMs tested — versions released in the first half of 2023 — produced code that did not compile, 95% of the updated versions released in the past year produced code that passed syntax checking. On the other hand, the security of the code has not improved much at all, with about half of the code generated by LLMs having a detectable OWASP Top-10 security vulnerability, according to Veracode.

Daily Tech Digest - May 24, 2025

Quote for the day:

“In my experience, there is only one motivation, and that is desire. No reasons or principle contain it or stand against it.” -- Jane Smiley

DanaBot botnet disrupted, QakBot leader indicted

Operation Endgame relies on help from a number of private sector cybersecurity companies (Sekoia, Zscaler, Crowdstrike, Proofpoint, Fox-IT, ESET, and others), non-profits such as Shadowserver and white-hat groups like Cryptolaemus. “The takedown of DanaBot represents a significant blow not just to an eCrime operation but to a cyber capability that has appeared to align Russian government interests. The case (…) highlights why we must view certain Russian eCrime groups through a political lens — as extensions of state power rather than mere criminal enterprises,” Crowdstrike commented the DanaBot disruption. ... “We’ve previously seen disruptions have significant impacts on the threat landscape. For example, after last year’s Operation Endgame disruption, the initial access malware associated with the disruption as well as actors who used the malware largely disappeared from the email threat landscape,” Selena Larson, Staff Threat Researcher at Proofpoint, told Help Net Security. “Cybercriminal disruptions and law enforcement actions not only impair malware functionality and use but also impose cost to threat actors by forcing them to change their tactics, cause mistrust in the criminal ecosystem, and potentially make criminals think about finding a different career.”

AI in Cybersecurity: Protecting Against Evolving Digital Threats

Beyond detecting threats, AI excels at automating repetitive security tasks. Tasks like patching vulnerabilities, filtering malicious traffic, and conducting compliance checks can be time-consuming. AI’s speed and precision in handling these tasks free up cybersecurity professionals to focus on complex problem-solving. ... The integration of AI into cybersecurity raises ethical questions that must be addressed. Privacy concerns are at the forefront, as AI systems often rely on extensive data collection. This creates potential risks for mishandling or misuse of sensitive information. Additionally, AI’s capabilities for surveillance can lead to overreach. Governments and corporations may deploy AI tools for monitoring activities under the guise of security, potentially infringing on individual rights. There is also the risk of malicious actors repurposing legitimate AI tools for nefarious purposes. Clear guidelines and robust governance are crucial to ensuring responsible AI deployment in cybersecurity. ... The growing role of AI in cybersecurity necessitates strong regulatory frameworks. Governments and organizations are working to establish policies that address AI’s ethical and operational challenges in this field. Transparency in AI decision-making processes and standardized best practices are among the key priorities.

Open MPIC project defends against BGP attacks on certificate validation

MPIC is a method to enhance the security of certificate issuance by validating domain ownership and CA checks from multiple network vantage points. It helps prevent BGP hijacking by ensuring that validation checks return consistent results from different geographical locations. The goal is to make it more difficult for threat actors to compromise certificate issuance by redirecting internet routes. ... Open MPIC operates through a parallel validation architecture that maximizes efficiency while maintaining security. When a domain validation check is initiated, the framework simultaneously queries all configured perspectives and collects their results. “If you have 10 perspectives, then it basically asks all 10 perspectives at the same time, and then it will collect the results and determine the quorum and give you a thumbs up or thumbs down,” Sharkov said. This approach introduces some unavoidable latency, but the implementation minimizes performance impact through parallelization. Sharkov noted that the latency is still just a fraction of a second. ... The open source nature of the project addresses a significant challenge for the industry. While large certificate authorities often have the resources to build their own solutions, many smaller CAs would struggle with the technical and infrastructure requirements of multi-perspective validation.

How to Close the Gap Between Potential and Reality in Tech Implementation

First, there has to be alignment between the business and tech sides. So, I’ve seen in many institutions that there’s not complete alignment between both. And where they could be starting, they sometimes separate and they go in opposite directions. Because at the end of the day, let’s face it, we’re all looking at how it will help ourselves. Secondly, it’s just the planning, ensuring that you check all the boxes and have a strong implementation plan. One recent customer who just joined Backbase: One of the things I loved about what they brought to the kickoff call was what success looked like to them for implementation. So, they had the work stream, whether the core integration, the call center, their data strategy, or their security requirements. Then, they had the leader who was the overall owner and then they had the other owners of each work stream. Then, they defined success criteria with the KPIs associated with those success criteria. ... Many folks forget that they are, most of the time, still running on a legacy platform. So, for me, success is when they decommission that legacy platform and a hundred percent of their members or customers are on Backbase. That’s one of the very important internal KPIs.

How AIOps sharpens cybersecurity posture in the age of cyber threats

The good news is, AIOps platforms are built to scale with complexity, adapting to new environments, users, and risks as they develop. And organizations can feel reassured that their digital vulnerabilities are safeguarded for the long term. For example, modern methods of attack, such as hyperjacking, can be identified and mitigated with AIOps. This form of attack in cloud security is where a threat actor gains control of the hypervisor – the software that manages virtual machines on a physical server. It allows them to then take over the virtual machines running on that hypervisor. What makes hyperjacking especially dangerous is that it operates beneath the guest operating systems, effectively evading traditional monitoring tools that rely on visibility within the virtual machines. As a result, systems lacking deep observability are the most vulnerable. This makes the advanced observability capabilities of AIOps essential for detecting and responding to such stealthy threats. Naturally, this evolving scope of digital malice also requires compliance rules to be frequently reviewed. When correctly configured, AIOps can support organizations by interpreting the latest guidelines and swiftly identifying the data deviations that would otherwise incur penalties.

Johnson & Johnson Taps AI to Advance Surgery, Drug Discovery

J&J's Medical Engagement AI redefines care delivery, identifying 75,000 U.S. patients with unmet needs across seven disease areas, including oncology. Its analytics engine processes electronic health records and clinical guidelines to highlight patients missing optimal treatments. A New York oncologist, using J&J's insights, adjusted treatment for 20 patients in 2024, improving the chances of survival. The platform engages over 5,000 providers, empowering medical science liaisons with real-time data. It helps the AI innovation team turn overwhelming data into an advantage. Transparent data practices and a focus on patient outcomes align with J&J's ethical standards, making this a model that bridges tech and care. ... J&J's AI strategy rests on five ethical pillars, including fairness, privacy, security, responsibility and transparency. It aims to deliver AI solutions that benefit all stakeholders equitably. The stakeholders and users understand the methods through which datasets are collected and how external influences, such as biases, may affect them. Bias is mitigated through annual data audits, privacy is upheld with encrypted storage and consent protocols, and on top of it is AI-driven cybersecurity monitoring. A training program, launched in 2024, equipped 10,000 employees to handle sensitive data.

Surveillance tech outgrows face ID

Many oppose facial recognition technology because it jeopardizes privacy, civil liberties, and personal security. It enables constant surveillance and raises the specter of a dystopian future in which people feel afraid to exercise free speech.Another issue is that one’s face can’t be changed like a password can, so if face-recognition data is stolen or sold on the Dark Web, there’s little anyone can do about the resulting identity theft and other harms. .... You can be identified by your gait (how you walk). And surveillance cameras now use AI-powered video analytics to track behavior, not just faces. They can follow you based on your clothing, the bag you carry, and your movement patterns, stitching together your path across a city or a stadium without ever needing a clear shot of your face. The truth is that face recognition is just the most visible part of a much larger system of surveillance. When public concern about face recognition causes bans or restrictions, governments, companies, and other organizations simply circumvent that concern by deploying other technologies from a large and growing menu of options. Whether we’re IT professionals, law enforcement technologists, security specialists, or privacy advocates, it’s important to incorporate the new identification technologies into our thinking, and face the new reality that face recognition is just one technology among many.

How Ready Is NTN To Go To Scale?

Non-Terrestrial Networks (NTNs) represent a pivotal advancement in global communications, designed to extend connectivity far beyond the limits of ground-based infrastructure. By leveraging spaceborne and airborne assets—such as Low Earth Orbit (LEO), Medium Earth Orbit (MEO), and Geostationary (GEO) satellites, as well as High-Altitude Platform Stations (HAPS) and UAVs—NTNs enable seamless coverage in regions previously considered unreachable. Whether traversing remote deserts, deep oceans, or mountainous terrain, NTNs provide reliable, scalable connectivity where traditional terrestrial networks fall short or are economically unviable. This paradigm shift is not merely about extending signal reach; it’s about enabling entirely new categories of applications and industries to thrive in real time. ... A core feature of NTNs is their use of varied orbital altitudes, each offering distinct performance characteristics. Low Earth Orbit (LEO) satellites (altitudes of 500–2,000 km) are known for their low latency (20–50 ms) and are ideal for real-time services. Medium Earth Orbit (MEO) systems (2,000–35,000 km) strike a balance between coverage and latency and are often used in navigation and communications. Geostationary Orbit (GEO) satellites, positioned at ~35,786 km, provide wide-area coverage from a fixed position relative to Earth’s rotation—particularly useful for broadcast and constant-area monitoring.

Enterprises are wasting the cloud’s potential

One major key to achieving success with cloud computing is training and educating employees. Although the adoption of cloud technology signifies a significant change, numerous companies overlook the importance of equipping their staff with the technical expertise and strategic acumen to capitalize on its potential benefits. IT teams that lack expertise in cloud services may use cloud resources inefficiently or ineffectively. Business leaders who are unfamiliar with cloud tools often struggle to leverage data-driven insights that could drive innovation. Employees relying on cloud-based applications might not fully utilize all their functionality due to insufficient training. These skill gaps lead to dissatisfaction with cloud services, and the company doesn’t benefit from its investments in cloud infrastructure. ... The cloud is a tool for transforming operations rather than just another piece of IT equipment. Companies can refine their approach to the cloud by establishing effective governance structures and providing employees with training on the optimal utilization of cloud technology. Once they engage architects and synchronize cloud efforts with business objectives, most companies will see tangible results: cost savings, system efficiency, and increased innovation.

The battle to AI-enable the web: NLweb and what enterprises need to know

NLWeb enables websites to easily add AI-powered conversational interfaces, effectively turning any website into an AI app where users can query content using natural language. NLWeb isn’t necessarily about competing with other protocols; rather, it builds on top of them. The new protocol uses existing structured data formats like RSS, and each NLWeb instance functions as an MCP server. “The idea behind NLWeb is it is a way for anyone who has a website or an API already to very easily make their website or their API an agentic application,” Microsoft CTO Kevin Scott said during his Build 2025 keynote. “You really can think about it a little bit like HTML for the agentic web.” ... “NLWeb leverages the best practices and standards developed over the past decade on the open web and makes them available to LLMs,” Odewahn told VentureBeat. “Companies have long spent time optimizing this kind of metadata for SEO and other marketing purposes, but now they can take advantage of this wealth of data to make their own internal AI smarter and more capable with NLWeb.” ... “NLWeb provides a great way to open this information to your internal LLMs so that you don’t have to go hunting and pecking to find it,” Odewahn said. “As a publisher, you can add your own metadata using schema.org standard and use NLWeb internally as an MCP server to make it available for internal use.”

Daily Tech Digest - February 19, 2025

Quote for the day:

"Go confidently in the direction of your dreams. Live the life you have imagined." -– Henry David Thoreau

Why Observability Needs To Go Headless

Not all logs have long-term value, but that’s one of the advantages of headless observability and decoupled storage. Teams have the freedom and flexibility to determine which logs should be retained for longer periods. Web application firewall (WAF) and other security logs can be retained over the long term and made available to cybersecurity teams and threat hunters. Other application logs can provide long-term insights into how resources are being used for capacity planning and anomaly detection. Let’s take a closer look at a real, tangible use case where observability data can be valuable for other teams: real user monitoring (RUM). In the realm of observability, RUM allows teams to proactively monitor how end users are experiencing web applications. Issues like slow page loads can be mitigated before they frustrate users. Beyond observability, RUM data can also provide insights into how your end users are interacting with your brand and your products. This data is invaluable for marketing, advertising and leadership teams that need to plan strategy. ... As a real-world example, many enterprises use CDN log data for real user monitoring. In the short term, monitoring CDNs is important for ensuring good user experiences and fast loading times of digital assets. However, being able to retain huge volumes of log data long term and cost-effectively provides certain advantages to enterprises.

Why the CIO role should be split in two

The fact is that within enterprises, existing architecture is overly complex, often including new digital systems interconnected with legacy systems. This ‘hybrid’ architecture is a combination of best and bad practice. When there is an outage, the new digital platforms can invariably be restored to recover business process support. But because they do not operate in isolation, instead connecting with legacy technologies, business operations themselves may not fully recover if the legacy systems continue to be impacted by the outage. For most enterprises stuck in this hybrid state, the way forward is to be more discipline around architecture. ... Simplifying architecture at an enterprise level is something the CIO and CISO should work together concurrently as a shared goal. The benefits of doing so will accrue over time rather than immediately, hence there can be some reluctance to prioritize. ... What does all this have to do with my opening discussion about the CIO and complementary IT executive roles? Splitting the CIO role into smaller and smaller pieces would be okay if doing so led to better outcomes. But I would argue that examples like the ones above show that the multiple-exec approach is not a success story we should be bragging about. In this structure, the two CIOs would share ownership of the IT strategy.

Generative AI vs. the software developer

AI is not going to turn your customer support people (Elvis bless them) into senior software developers. A customer support person might be able to think “I need to track the connection between items in inventory, the customer’s shopping cart, and the discount pricing for a given item,” but unless that person also knows how to code, they will have a seriously hard time instructing an AI model to generate the code they need. Most likely, they aren’t going to know if the code the AI produces even runs, let alone works correctly. But AI can help actual developers in many ways. It can look at existing code you have written and help you produce the next thing that you need to write. It can even write large routines and classes that you ask it to. But it is not going to create the things you need without you having a large say in what that is. You need to know how to craft a prompt to get precisely what is needed. ... Now, that prompt will be pretty effective in getting what is asked for. But the trick here, obviously, is that you have to know what a React component is, what Tailwind is, the fact that you want tests, what TypeScript is, what null is, and that you’d even need to handle missing values. There is a lot of knowledge and experience wrapped up in that prompt, and it’s not something that an inexperienced developer, or certainly a non-developer, would be able to write.

Beyond the Screen: Humanising Digital Learning

Digital learning holds a lot of promise, aiming to bring the most dynamic and engaging elements of in-person training into the digital space. Interactive tools like quizzes, breakout rooms, and mini-tasks demonstrate just how far we’ve come in replicating real-world engagement online. However, we continue to see issues with retention and follow through. Recent research shows that 66% of employees still find on-the-job learning to be more effective than formal online courses. This disconnect often stems from a lack of deep, meaningful engagement. Without it, employees are less likely to retain knowledge or apply their skills effectively in the workplace. This is particularly crucial when it comes to human skills—broader soft skills like communication, emotional intelligence, and critical thinking. Unlike technical skills that are typically learned ‘by the book’, softer skills are learned and applied every day. The solution lies in moving beyond passive consumption to real-world, interactive learning simulations. ... The shift to digital learning offers incredible potential, but realising that potential requires a thoughtful approach. By embracing AI-powered technologies and prioritising interactive, personalised and bite-sized content, organisations can create learning experiences that are engaging, practical and transformative.

Shadow AI: How unapproved AI apps are compromising security, and what you can do about it

Shadow AI introduces significant risks, including accidental data breaches, compliance violations and reputational damage. It’s the digital steroid that allows those using it to get more detailed work done in less time, often beating deadlines. Entire departments have shadow AI apps they use to squeeze more productivity into fewer hours. “I see this every week,” Vineet Arora, CTO at WinWire, recently told VentureBeat. “Departments jump on unsanctioned AI solutions because the immediate benefits are too tempting to ignore.” ... “If you paste source code or financial data, it effectively lives inside that model,” Golan warned. Arora and Golan find companies training public models defaulting to using shadow AI apps for a wide variety of complex tasks. Once proprietary data gets into a public-domain model, more significant challenges begin for any organization. It’s especially challenging for publicly held organizations that often have significant compliance and regulatory requirements. Golan pointed to the coming EU AI Act, which “could dwarf even the GDPR in fines,” and warns that regulated sectors in the U.S. risk penalties if private data flows into unapproved AI tools. There’s also the risk of runtime vulnerabilities and prompt injection attacks that traditional endpoint security and data loss prevention (DLP) systems and platforms aren’t designed to detect and stop.

Think being CISO of a cybersecurity vendor is easy? Think again

When people in this industry hear that a CISO is working at a cybersecurity vendor, it can trigger a number of assumptions — many of them misguided. There’s a stereotype that the role isn’t “real” CISO work, that it’s more akin to being a field CISO, someone primarily outward-facing and focused on supporting sales or amplifying the brand. The assumption goes something like this: How hard can it be to secure a security company, and isn’t the “real” work done at companies outside of this bubble? ... Some might think that working at a security company limits your perspective of what’s out there in the broader industry, but I found the opposite to be true. I gained a deeper understanding of how organizations evaluate security solutions and what they truly care about. I saw firsthand the challenges customers faced when implementing security tools, and that experience gave me empathy, insight, and a renewed ability to speak their language. Now that I’m back in industry, I’m bringing that perspective with me. The transition wasn’t a step “down” or a shift away from anything; it was just the next phase in my career. Security leadership is security leadership, no matter where you practice it. The challenges remain complex, the responsibilities remain vast, and the importance of aligning security with business outcomes remains paramount.

Lack of regulations, oversight in health care IT can cause harm

Increasingly, health care organizations have outsourced their health IT infrastructure to companies owned and operated by private equity, venture capital and Big Tech firms that view them as platforms to experiment with unproven AI and machine-learning tools. "The unregulated integration of AI tools into these systems will make it even harder to protect patients' rights," Appelbaum said. "Moreover, because these records contain so much information and are centralized, they are among the most lucrative targets for cyberattacks and hackers," Batt said, noting that in 2024, data breaches exposed the health records of more than 200 million Americans. As a result, health care organizations must now invest billions more in cybersecurity systems owned and operated by venture capital, private equity and Big Tech. The authors argue that the federal government is once again behind in setting safeguards for the adoption of new health IT, and that the lessons from 30 years of attempts to set adequate standards for information-sharing in electronic health systems—as detailed in these reports—should spur regulators to act quickly and rein in unregulated financial activities in health IT. Batt explained, "The history of the health IT implementation and the lack of sufficient regulatory oversight and enforcement of standards should give us great pause for the current enthusiasm over the adoption of AI and machine learning in health information systems."

The Future of Data: How Decision Intelligence is Revolutionizing Data

Decision Intelligence is an interdisciplinary field that uses AI to enhance all aspects of decision-making across all areas of a Business. It blends concepts of Data Science (statistics, machine learning, AI, analytics) with Behavioral Sciences (psychology, neuroscience, economics, and managerial sciences) to understand how decisions are made and how outcomes are measured. ... Decision Intelligence (DI) can be considered a subset where it uses AI to build a reliable data foundation by collecting, organizing, and connecting data and then applying AI and analytics to turn that data into useful insights for better decision-making. In short, while AI provides the technology to mimic human intelligence, DI focuses on applying that technology to improve how decisions are made. ... You can use any of your machine learning models, like regression models, classification models, time series forecasting models, clustering algorithms, or reinforcement learning for implementing Decision Intelligence. These machine learning will help identify patterns in the data and make predictions based on those patterns, but decision intelligence will take that information one step further by incorporating it into a broader framework that can actively guide the decision-making process by considering the predictions and the potential outcomes and consequences of different choices.

ManpowerGroup exec explains how to manage an AI workforce

It’s not just a technology anymore. We are looking for individuals that have the industry experience. We can take somebody with industry experience and train them on the technical part of the job. “It’s a lot harder for us to take somebody with the technical skills and teach them how the industry works. I think there’s a focus on looking at the soft skills: the problem solving, the complex reasoning ability, and communications. Because it’s not just developing AI for the sake of software technology; it’s to address that larger business problem. It’s about looking at all of the business functions, and taking all of that into consideration. ... The problem is [that] the gap is getting wider between those employees who understand AI technology and are willing to learn more about it and those who don’t want to have anything to do with it. But I think everybody will be a technologist, eventually. It’s going to be talent augmented by technology. ... “There are so many things, and it’s happening so fast. So, we are still learning as fast as we can. We’re trying to understand what the impact of AI will be, and how it will change our business models. Even from a talent organization like ours, which is providing global talent solutions, what does that do for us? Now, our company is going to start looking for your talent plus the AI agents you’ll need. So AI becomes part of a hiring solution.

Debunking the AI Hype: Inside Real Hacker Tactics

While headlines are trumpeting AI as the one-size-fits-all new secret weapon for cybercriminals, the statistics—again, so far—are telling a very different story. In fact, after poring over the data, Picus Labs found no meaningful upswing in AI-based tactics in 2024. Yes, adversaries have started incorporating AI for efficiency gains, such as crafting more credible phishing emails or creating/ debugging malicious code, but they haven't yet tapped AI's transformational power in the vast majority of their attacks so far. In fact, the data from the Red Report 2025 shows that you can still thwart the majority of attacks by focusing on tried-and-true TTPs. ... Attackers are increasingly targeting password stores, browser-stored credentials, and cached logins, leveraging stolen keys to escalate privileges and spread within networks. This threefold jump underscores the urgent need for ongoing and robust credential management combined with proactive threat detection. Modern infostealer malware orchestrates multi-stage style heists blending stealth, automation, and persistence. With legitimate processes cloaking malicious operations and actual day-to-day network traffic hiding nefarious data uploads, bad actors can exfiltrate data right under your security team's proverbial nose, no Hollywood-style "smash-and-grab" needed. Think of it as the digital equivalent of a perfectly choreographed burglary.

Daily Tech Digest - July 10, 2024

How platform teams lead to better, faster, stronger enterprises

Platform teams are uniquely equipped to optimize resource allocation because they sit in between developers and the cloud infrastructure and compute that developers need, and are able to maximize the efficiency and effectiveness of software development processes. With their unique set of skills and expertise, they effectively collaborate with other teams, including developers, data scientists, and operations teams, to accurately understand their needs and pain points. Using a product approach, platform teams remove barriers for developers and operations teams by offering shared services for developer self-service, enabling faster modernization within organizational boundaries and automation to simplify the management of applications and Kubernetes clusters in the cloud. Fostering a culture of innovation, platform teams play a crucial role in keeping the organization at the forefront of emerging trends and technologies. This enables enterprises to provide innovative solutions that set them apart in the market.

Developing An AI Uuse Policy

An AI Use Policy is designed to ensure that any AI technology used by your business is done so in a safe, reliable and appropriate manner that minimises risks. It should be developed to inform and guide your employees on how AI can be used within your business. ... Perhaps the most important part for the majority of your employees, set specific do’s and don’ts for inputs and outputs. This is to ensure compliance with data security, privacy and ethical standards. For example, “Don’t input any company confidential, commercially sensitive or proprietary information”, “Don’t use AI tools in a way that could inadvertently perpetuate or reinforce bias” and “Don’t input any customer or co-worker’s personal data”. For outputs, guidance can reiterate to staff the potential for misinformation or ‘hallucinations’ generated by AI. Consider rules such as “Clearly label any AI generated content”, “Don’t share any output without careful fact-checking” or “Make sure that a human has the final decision when using AI to help make a decision which could impact any living person

Synergy between IoT and blockchain transforming operational efficiency

The synergy between the two technologies is integral to achieving Industry 4.0 goals, including digital transformation, decentralised connectivity, and smart industry advancements. Via this integration, organisations can achieve real-time visibility into production operations, optimise supply chain processes, and enhance overall efficiency. ... In regulated industries like pharmaceutical manufacturing, where compliance is crucial, integrating IoT and Blockchain lets companies onboard suppliers to upload raw material info, batch numbers, and quality checks to a blockchain ledger. IoT devices automate data acquisition during manufacturing and storage, ensuring data integrity and transparency. In smart city ecosystems, local authorities share data with service providers for waste management, traffic updates, and more. Traffic data from sensors can be securely uploaded to a blockchain, where third-party services like food delivery and ridesharing can access it to optimise operations. Logistics companies use IoT systems to gather data on location and handling, which is uploaded to a blockchain ledger to track goods, estimate delivery time, and provide real-time updates.

Ignore Li-ion fire risks at your peril

Li-ion batteries are prone to destructive and hard-to-control fires. There have been several reported incidents in data centers, some of which have led to serious outages, but they are not well-documented or systematically studied. ... A commonly held view is that Li-ion’s fire risk in the data center is overstated, partly as a result of marketing by vendors of alternative chemistries such as salt and nickel-zinc. If these products are promoted as a “safe” alternative, then it will (it is speculated) create a perception that Li-ion is “unsafe.” After assessing the evidence, examining the science, and hearing from data center operators at recent member meetings, Uptime Institute is taking a cautious and practicable stance at this point. While it is true that Li-ion batteries have a higher risk of fire compared with other chemistries, and these fires are particularly problematic, Uptime Institute engineers do not think Li-ion batteries should be rejected out of hand. ... Data center builders and operators should carefully consider the benefits of Li-ion batteries alongside the risks. As well as the obvious risk of serious fires, there are financial and reputational risks in preparing for, avoiding, and responding to such incidents.

More than a CISO: the rise of the dual-titled IT leader

Dual-title roles give CISOs new levers to work with and more scope to drive strategic integration and alignment of cybersecurity within the organization. ... Belknap finds having his own team of engineers puts him in a stronger position when working with partners. When looking for support or assistance with a project, his team will have already built something, reducing the amount of work needed from the partner team. “This means we can lean on them to be responsible for the things that only they can do. I don’t have to pull them into the work that only I can do or the work that’s not aligned to their expertise,” he says. These dual-title roles also recognize how CISOs are increasingly operating as technology leaders and operators of the organization, according to Adam Ely, head of digital products at Fidelity Investments who was formerly the firm’s CISO and has a long history in security. Ely says that as CISOs typically work across an organization, know how the business lines work, and are day-to-day leaders of people and technology as well as crisis managers, it stands them in good stead for dual-title or more senior positions.

You Can’t Wish Away Technology Complexity

Every business succeeds because of technology. Every person gets paid by technology. The value of our currency itself is about technology. Of course, it is not only about technology. But tell that to the CFO or CLO. When it is about finance, there is very little pushback in saying it is all about money. When it is about legal, there is no push-back about it being about law. I’ve noticed only technologists pull back and say, “You’re right, it’s not about technology.” ... See what people often forget that technology complexity is cool on multiple levels. It gives us the ability to make different choices for stakeholders and customers (I mean real customers not stakeholders that think they are customers – note to business stakeholders, you and I get our paychecks from the same place, you are not my customer. Our customer is my customer). But while this complexity allows for choice, it also creates a dependency on understanding those choices. Or a dependency on a professional who does. I don’t pretend to understand medicine. That is why I ask doctors what to do.

Electronic Health Record Errors Are a Serious Problem

The exposure of healthcare records, in even minor ways, leaves patients highly vulnerable. “I never reached out to this woman [whose records were entered into my father’s], but I had all her contact information. I could have gone to her house and handed her the copy of the results I had found in my dad’s records,” Hollingsworth says. ... Data aggregators pose a further risk. These organizations may collect deidentified data to perform analyses on population-level health issues for both healthcare organizations and insurance companies. “Are they following the same security standards that we follow in the health care transaction world?” Ghanayem asks. “I don’t know.” ... Clear distinctions between important information fields must be made to cut down on adjacency errors. Concise patient summaries at the beginning of each record and usable search features may increase usability and decrease frustration that leads to the introduction of errors. And refining when alerts are issued can decrease alert fatigue, which may lead providers to simply ignore alerts even when they are valid.

Diversifying cyber teams to tackle complex threats

To make a significant change and deliver a more diverse cyber workforce, we need to focus on leadership and change our language and processes for recruitment. This takes courage and is the biggest challenge organizations face. Having a diverse team helps others see it is a place for them. It isn’t just about attracting talent; it’s also about openness and retaining talent. Organizations need to help individuals from diverse backgrounds to see themselves as role models who need to be out shouting about the opportunities within the sector. Diversity fosters a sense of belonging and inclusivity making the cybersecurity field more attractive to a wider range of individuals. When potential recruits see relatable role models within a team, it breaks down the traditional and somewhat homogenous perception of cybersecurity. This inclusivity is crucial for attracting talent from underrepresented groups, particularly women and minority groups, who may not have traditionally seen themselves in cybersecurity roles. A diverse team with strong role models creates a positive feedback loop.

Nanotechnology and SRE: Pioneering Precision in Performance

Nanotechnology offers the opportunity to transform SRE at the atomic level — addressing individual tasks, subtasks, and tickets. For example, extra-sensitive nanosensors can continuously monitor system performance metrics, including temperature, voltage, and processing load. When placed in data centers, these sensors enable real-time data collection and analysis, detecting electrical and mechanical issues before they escalate and extending the lifespan of technological components. Nanobots can be programmed to address hardware issues and routine maintenance tasks. Together, these technologies can integrate into a self-healing and continuously improving system in line with SRE principles. ... Nanotechnology can potentially transform SRE, leading to enhanced system reliability and performance. Nanotechnology-enabled solutions can allow more precise monitoring, optimization, and real-time improvements, supporting the key pillars of SRE. At the same time, the foundational principles of SRE can be applied to ensure the reliability of advanced nanotechnology systems.

Three Areas Where AI Can Make a Huge Difference Without Significant Job Risk

Doing a QC job can be annoying because even though the job is critical to the outcome, your non-QC peers and management treat you like a potentially avoidable annoyance. You stand in the way of shipping on time and at volume, potentially delaying or even eliminating performance-based bonuses. We are already discovering that to assure the quality of an AI-driven coding effort, a second AI is needed to assure the quality of the result because people just don’t like doing QC on code, particularly those who create it. ... In short, properly applied AI could highlight and help address problems that are critically reducing a company’s ability to perform to its full potential and preventing it from becoming a great place to work. ... Calculating an employee’s contribution and then using it to set compensation transparently should significantly reduce the number of employees who feel they are being treated unfairly by eliminating that unfairness or by showing them a path to improve their value and thus positively impact their pay.

Quote for the day:

"When you stop chasing the wrong things you give the right things a chance to catch you." -- Lolly Daskal