Daily Tech Digest by Kannan Subbiah: data science

Showing posts with label data science. Show all posts

Daily Tech Digest - July 13, 2026

Quote for the day:

“An entrepreneur is someone who jumps off a cliff and builds a plane on the way down.” -- Reid Hoffman

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AI in the Boardroom: What Directors Must Now Govern

The boardroom conversation around artificial intelligence has shifted from deciding whether to experiment to figuring out how to successfully govern the technology. While many company directors now use AI for their personal productivity, using a specific tool is vastly different from overseeing its safe and strategic deployment across an entire organization. As AI becomes deeply embedded in strategy, supply chains, and daily operations, it brings complex new risks, particularly in cybersecurity and external vendor management. Importantly, when an AI system makes a flawed decision or causes harm, accountability cannot be outsourced to a vendor or the algorithm itself; it remains firmly with the human leaders and the board. Currently, a significant expertise gap exists, with most boards lacking even one literate director, let alone a collective understanding of the topic. However, boards do not need to hire software engineers or data scientists. Instead, they need directors capable of asking sharp questions, evaluating risk, and connecting these new initiatives to broader business strategy. To close this gap, boards should focus on raising the technical literacy of all members rather than relying on a single expert. Practical first steps include auditing current usage, defining clear oversight responsibilities, establishing audit trails for automated decisions, and bringing in seasoned advisors to evaluate the overall management approach.

The Implementation Gap: Why Africa’s Digital Strategies Rarely Become Digital Reality

Despite having no shortage of ambitious national digital strategies, data protection laws, and broadband policies, African nations frequently struggle to turn these plans into reality. This persistent issue is known as the implementation gap. Governments often celebrate the launch of new policies but fail to dedicate the same energy to executing them. A major part of the problem is the false belief that simply purchasing new technology equals true digital transformation. In reality, buying new software means very little without also redesigning outdated business processes and improving institutional capabilities. The article identifies seven main hurdles holding back progress. First, shifting political leadership often disrupts long-term projects. Second, many public institutions still rely on old, paper-based administrative structures. Third, procurement focuses too much on acquiring technology instead of improving public outcomes. Fourth, government digital systems are often fragmented and unable to share information with each other. Fifth, cybersecurity is typically treated as a delayed afterthought rather than a built-in priority. Sixth, governments fail to invest enough in training civil servants and citizens to use these new tools. Finally, institutions frequently repeat the mistakes of past projects instead of learning from them. To succeed, the focus must shift from launching more strategies to building capable institutions that can steadily deliver real, lasting public value.

Upskilling for Emerging Industries Affected by Data Science

As data science transforms global industries, the demand and compensation for skilled professionals continue to rise. However, this well-paying field is also becoming highly competitive, meaning that simply landing a job is no longer enough to guarantee your long-term security in the workforce. To build a lasting career, continuous learning is essential to avoid falling behind in a rapidly shifting job market. The pace of rapid technological advancements dictates that traditional skills can very quickly become outdated, while brand new roles in specialized areas like artificial intelligence, renewable energy, cybersecurity, and blockchain consistently emerge. To succeed in these newer positions, data scientists must cultivate core traits such as adaptability, critical thinking, clear communication, and creativity. Employers actively seek out individuals who possess a growth mindset and can quickly adjust to new tools and complex challenges. Professionals can stay competitive by embracing varied educational strategies. This includes enrolling in targeted online courses through accessible educational platforms, attending industry workshops, and connecting with experienced mentors for personalized guidance. Additionally, volunteering for projects outside your normal duties and engaging with professional networks can provide practical experience. By treating your education as an ongoing journey, you can protect your career and easily pivot into new opportunities as the landscape changes.

Australian developers are losing half their day, most leaders have no idea

Australian software developers are currently spending the vast majority of their working hours on tasks outside of actual coding. Although engineering leaders often believe their teams are highly productive, studies show developers spend a mere sixteen percent of their day writing software. The rest of their time is consumed by navigating security protocols, complex deployment processes, and infrastructure monitoring. This significant gap between leadership perception and daily reality represents a major hidden cost for businesses today. The problem is heavily compounded by a lack of clear visibility into how software performs in live environments. When engineers cannot easily identify the root cause of system issues, they are forced to spend hours troubleshooting rather than creating new features. Furthermore, the rapid integration of artificial intelligence tools is adding a new layer of operational complexity. While artificial intelligence can speed up initial development, it also introduces unpredictable behaviors and risks that are very difficult to manage without proper oversight. To fix this ongoing productivity drain, organizations need to securely connect system performance data directly to developer workflows. By giving engineering teams clear, real-time insights into system health and AI behavior, leaders can reduce daily friction, minimize time wasted on resolving errors, and give developers their time back to focus on building reliable software.

Accountable Intelligence: Why India must get healthcare AI right

While artificial intelligence is transforming many industries, its role in healthcare carries significantly higher stakes. In most fields, an AI mistake causes mere inconvenience; in medicine, it can impact human lives. For this reason, India must adopt healthcare AI with strict accountability and clinical evidence. The country faces unique medical challenges, including a vast population, rising chronic diseases, and a divide in urban-rural access. AI offers practical solutions, such as quickly analyzing X-rays or flagging early signs of conditions like diabetic retinopathy, helping shift the system from reactive treatments to proactive care. However, achieving these benefits requires the right approach. AI is not meant to replace doctors. Instead, it serves as a valuable support system that reduces administrative workloads and highlights patterns that busy medical professionals might miss. To succeed in India, AI models cannot simply be imported; they must be trained and validated using diverse local data to ensure accuracy across different regions and demographics. Furthermore, developers must prioritize data privacy, clinical oversight, and transparent patient consent. Building genuine trust requires health technology companies to focus on proven clinical outcomes rather than just technological potential. Ultimately, the future of medicine is doctors and AI working together to strengthen patient care.

The AI Governance Gap: Why Traditional Security Controls Are Falling Behind

Traditional enterprise security was designed for a predictable world where applications behaved consistently and network traffic passed through centralized checkpoints. These conventional governance models are failing because artificial intelligence operates completely differently. AI is dynamic, changes based on user prompts, and is increasingly embedded directly into approved tools like productivity suites and web browsers. Because these interactions bypass traditional network filters, organizations face a massive visibility gap. They often cannot tell how AI is being used, what sensitive data is being shared, or what actions autonomous agents are taking on their behalf. Attempting to manage this by simply blocking unapproved AI apps is ineffective and often drives employees toward hidden shadow AI use. To close this gap, companies must move away from static application checklists and adopt source-level monitoring. This approach focuses on capturing real-time interactions, such as the exact prompts users send, the specific data flowing in, and the models' direct responses, right where the activity occurs. By prioritizing continuous, context-aware visibility over outright restriction, businesses can identify risky behavior regardless of which specific tool is being used. As AI becomes deeply woven into everyday workflows, effective governance will depend entirely on tracking how information moves through these intelligent systems rather than just monitoring standard network traffic.

On AI Ethics: Why Prompt Engineering Needs a Moral Compass

As the practice of giving instructions to artificial intelligence—often called prompt engineering—grows in demand, the need for a strong moral compass is becoming increasingly clear. Simply training an AI model well is not enough; the specific instructions given to these systems can independently create significant ethical dilemmas. Harmful prompts can easily amplify existing biases, expose private information, generate convincing misinformation, or be used for malicious exploitation. Recent guidance from Pope Leo XIV highlights that AI must serve humanity rather than concentrate power, warning against a purely profit-driven approach and calling for shared standards of social justice and accountability. The real-world consequences of poor AI ethics are already visible across multiple fields. Researchers note that mental health chatbots routinely violate established ethical standards through deceptive empathy and poor crisis management. Furthermore, AI tools are creating complex, hidden security threats, as automated programs operate within approved workflows but still execute harmful actions. Because the speed of modern AI adoption is entirely unprecedented, technology and security professionals can no longer assume a system is safe just because it functions as designed. Moving forward, organizations must actively govern how their AI behaves, clearly define ethical boundaries, and closely monitor both human and machine activities to properly protect their daily operations.

Claude Security Risks: What Your Security Team Needs to Know

Using AI tools like Claude in the workplace presents serious security challenges for companies, extending far beyond the software itself. The primary danger comes from how employees use the tool. When workers paste full reports, large spreadsheets, or confidential documents into the platform for analysis, they unknowingly expose sensitive company information and intellectual property. Because these bulk uploads happen without internal oversight, companies lose track of their data, which can lead to major compliance and audit failures. Another significant issue is context leakage. Information shared in one conversation can easily influence the answers generated in later sessions. If a team discusses proprietary processes or confidential insights, those details might unintentionally surface in future responses within shared workspaces. Furthermore, the boundaries between different types of accounts are often blurred. Employees frequently switch between personal accounts, shared team spaces, and official enterprise environments. This lack of clear separation weakens overall data governance, allowing regulated or sensitive information to drift outside of approved, secure areas. Ultimately, these blind spots create serious vulnerabilities, including accidental data disclosure and incomplete legal responses. To protect their assets, businesses must recognize that the most significant risk lies in unmonitored human behavior and a lack of clear access boundaries.

Manual Workarounds as Operational Risk Get Louder

When employees constantly create manual workarounds to bypass clunky systems, they are not simply trying to be difficult; they are attempting to keep the business moving forward. However, these temporary fixes quickly evolve into significant operational risks over time. Once a shortcut becomes a regular habit, it replaces official workflows and creates undocumented, fragile systems. These shadow processes—like hidden spreadsheets or email approvals—mask the true state of operations and create severe vulnerabilities, especially when they involve financial data or regulatory compliance. Furthermore, workarounds often rely entirely on a single person's memory, creating a dangerous dependency that falls apart if that individual leaves or during a major emergency. To protect the organization, leaders must view these side paths not as employee indiscipline, but as clear signals of failing internal infrastructure. Rather than demanding people work harder, management needs to thoroughly audit these hidden habits and address the core root causes of the friction. Every workaround that is allowed to continue must be assigned a specific owner, given a strict review date, and carefully evaluated for its overall business impact. By replacing these fragile, manual patches with permanently improved systems, organizations can maintain clear visibility, ensure steady control, and safely scale their daily operations.

Beyond Physical Security. Why FMs are strategic risk leaders

Facility management is no longer just about maintaining physical buildings. Because organizations face increasingly complex threats, from severe weather and cyberattacks to global supply chain delays, the roles of facility management and security are rapidly merging. Today, a company's facilities are critical environments that directly impact business operations, employee well-being, and overall corporate reputation. This shift requires facility leaders to step into highly strategic roles. They must now deeply understand risk assessment, crisis planning, and how to effectively integrate new technologies to keep operations running smoothly during emergencies. Instead of working in isolation, these professionals collaborate closely with security, IT, human resources, and executive teams to build a strong defense against potential disruptions. Smart building systems and advanced monitoring tools help identify problems early, but they require skilled people and clear rules to be truly effective. Furthermore, resilience is no longer treated as a separate emergency plan; it is becoming a daily habit woven into how companies choose suppliers, design workspaces, and manage their environmental footprint. Employees also expect to feel safe and supported in their daily work environments. By combining daily operational excellence with long-term strategic planning, modern facility leaders help organizations protect their staff, maintain steady operations, and ensure lasting stability.

Daily Tech Digest - February 28, 2026

Quote for the day:

"Stories are the single most powerful weapon in a leader's arsenal." -- Howard Gardner

AI ambitions collide with legacy integration problems

Many enterprises have moved beyond experimentation and are preparing for formal deployment. The survey found that 85% have begun adopting AI or expect to do so within the next 12 months. Respondents also reported efforts to formalise AI governance, reflecting greater attention to risk, accountability and oversight. ... Integration sits at the centre of that tension. AI initiatives often depend on clean data, consistent definitions and reliable access across multiple applications, requirements that legacy estates can complicate. The survey links these constraints to compliance risks, including data retention, access controls and auditability across connected systems. ... Security and privacy concerns featured prominently. Data privacy across systems was cited as a top risk by 49% of respondents, while 48% said they were concerned about third parties handling sensitive data. The results highlight the difficulty of managing information flows when AI systems interact with multiple internal applications and external providers. Governance approaches varied. Fewer than half (47%) said board-level reporting forms part of risk management for AI and related technology work, suggesting uneven executive oversight as AI moves into operational settings where incidents can carry regulatory and reputational consequences. ... Despite pressure to move quickly on AI initiatives, respondents said engineering quality remains a priority.

Striking the Right Balance Between Automation and Manual Processes in IT

Rather than thinking of applying AI wherever possible and over-automating, leaders should think about the most beneficial uses of the technology and begin implementation of the technology in those areas first before expanding further. Automation is a powerful tool, but humans are the most powerful tool in the IT stack. Let’s discuss how today’s IT leaders can strike the right balance between automation and manual processes. ... Even with the many benefits of automation, human-led processes still reign supreme in certain areas. For example, optimal IT operations happen at the intersection of tools and teamwork. IT teams must still foster a collaborative culture, working with other departments to ensure cross team visibility and alignment on business goals. While the latest AI technology can help in these efforts, ultimately, humans must do this collaborative work. Team dynamics can also be complex at times. Conflict resolution and major team decisions are not things that automation can solve. Moreover, if there is a critical system issue, DBAs must be able to work with IT leaders to resolve this issue and forge a path forward. Finally, manual processes are often necessitated by convoluted workflows. Many DBA teams have workflows in which every step is a set of if-then-else decisions, with each possible outcome also encumbered with many if-then decisions cascading through multiple levels of decisions.

Translating data science capabilities into business ROI

The fundamental challenge in demonstrating data science ROI is that most analytics infrastructure feels optional until it becomes essential. During normal operations, executives tolerate delays in reporting and gaps in visibility. During a crisis, those same gaps become existential threats. ... The turning point came when I realized we weren’t facing a data problem or a technology problem. We were facing a decision-making problem. Our leadership needed to maintain operational stability for a multi-trillion-dollar asset manager during unprecedented disruption. Every day without visibility meant delayed decisions, missed opportunities, and compounding uncertainty. ... Speed-to-value often trumps technical sophistication. The COVID dashboard taught me this lesson definitively. We could have spent months building a comprehensive data warehouse with sophisticated ETL pipelines and machine learning-powered forecasting. Instead, we focused ruthlessly on the minimum viable solution that executives needed immediately. ... Strategic positioning creates a disproportionate impact. I served as strategic architect for a major product repositioning — a multi-million-dollar initiative essential for our competitive positioning. My data-backed strategies produced immediate, quantifiable market share gains and resulted in substantially larger deal sizes and accelerated acquisition rates that fundamentally altered our market position.

The reliability cost of default timeouts

Many widely used libraries and systems default to infinite or extremely large timeouts. In Java, common HTTP clients treat a timeout of zero as “wait indefinitely” unless explicitly configured. In Python, requests will wait indefinitely unless a timeout is set explicitly. The Fetch API does not define a built-in timeout at all. These defaults aren’t careless. They’re intentionally generic. Libraries optimize for the correctness of a single request because they can’t know what “too slow” means for your system. Survivability under partial failure is left to the application. ... Long timeouts can also mask deeper design problems. If a request regularly times out because it returns thousands of items, the issue isn’t the timeout itself. It’s missing pagination or poor request shaping. By optimizing for individual request success, teams unintentionally trade away system-level resilience. ... A timeout defines where a failure is allowed to stop. Without timeouts, a single slow dependency can quietly consume threads, connections and memory across the system. With well-chosen timeouts, slowness stays contained instead of spreading into a system-wide failure. ... A timeout is a decision about value. Past a certain point, waiting longer does not improve user experience. It increases the amount of wasted work a system performs after the user has already left. A timeout is also a decision about containment. Without bounded waits, partial failures turn into system-wide failures through resource exhaustion: blocked threads, saturated pools, growing queues and cascading latency.

From dashboards to decisions: How streaming data transforms vertical software

For years, the standard for vertical software has been the nightly sync. You collect data all day, run a massive batch job at 2:00 AM, and provide your customers with a clean report the next morning. In a world of 2026, that delay is becoming a liability rather than a best practice. ... Data streaming isn’t just about moving bits faster; it’s about changing the fundamental value proposition of your application. Instead of being a system of record that tells a user what happened, your software becomes a system of agency that tells them what is happening right now. This shift requires a mental move away from static databases toward event-driven architectures. You’re no longer just storing a “state” (like current inventory); you’re capturing every “event” (every scan, every sale, every sensor ping) that leads to that state. ... One of the biggest mistakes I see software leaders make is treating real-time data as a “table stakes” feature that they give away for free. Streaming infrastructure is expensive to run and even more expensive to maintain. If you bake these costs into your standard subscription without a clear monetization strategy, you’ll watch your gross margins shrink as your customers’ data volumes grow. ... When you process data at the edge, you’re also solving the “data gravity” problem. Sending thousands of high-frequency sensor pings from a factory floor to the cloud just to filter out the noise is a waste of bandwidth and money.

MCP leaves much to be desired when it comes to data privacy and security

From a data privacy standpoint, one of the major issues is data leakage, while from a security perspective, there are several things that may cause issues, including prompt injections, difficulty in distinguishing between verified and unverified servers, and the fact that MCP servers sit below typical security controls. ... Fulkerson went on to say that runtime execution is another issue, and legacy tools for enforcing policies and privacy are static and don’t get enforced at runtime. When you’re dealing with non-deterministic systems, there needs to be a way to verifiably enforce policies at runtime execution because the blast radius of runtime data access has outgrown the protection mechanisms organizations have. He believes that confidential AI is the solution to these problems. Confidential AI builds on the properties of confidential computing, which involves using hardware that has an encrypted cache, allowing data and inference to be run inside an encrypted environment. While this helps prove that data is encrypted and nobody can see it, it doesn’t help with the governance challenge, which is where Fulkerson says confidential AI comes in. Confidential AI treats everything as a resource with its own set of policies that are cryptographically encoded. For example, you could limit an agent to only be able to talk to a specific agent, or only allow it to communicate with resources on a particular subnet.

3 Ways OT-IT Integration Helps Energy and Utilities Providers Modernize Grid Operations

Increasingly, energy providers are turning to digital twins to model and simulate critical infrastructure across generation, transmission and distribution environments. By feeding live telemetry from supervisory control and data acquisition systems, intelligent electronic devices and other OT assets into IT-based simulation platforms, utilities can create real-time digital replicas of substations, turbines, transformers and even entire grid segments. This enables teams to test load-balancing strategies, maintenance schedules or DER integrations without disrupting service. ... Private 5G networks offer a compelling alternative. Designed for high reliability and low latency, private 5G can operate effectively in interference-heavy environments such as substations or generation facilities. When paired with TSN, utilities can achieve deterministic, sub-millisecond communication between protection systems, controllers and analytics platforms. ... Federated machine learning allows utilities to train AI models locally at the edge — analyzing equipment performance, detecting anomalies and refining predictive maintenance strategies — without centralizing raw operational data. For industries such as energy and oil, remote sites can run local anomaly detection models tailored to site-specific conditions, while still sharing insights that strengthen enterprisewide safety and operational protocols.

Even if AI demand fades, India need not worry - about data centres

AI pushes rack densities from ~5–10kW to 50–100kW+, making liquid cooling, greater power capacity, and purpose‑built ‘AI‑ready’ Data Centre campuses essential — whether for regional training clusters or dense inference. What makes a Data Centre AI-ready is the ability to support advanced cooling, predictable scalability and direct access to clouds, networks and partners in a sustainable manner. ... In India, enterprises are rapidly adopting hybrid and multi-cloud architectures as they modernise their digital infrastructure. Domestic enterprises, particularly in BFSI and broking, are moving away from in-house data centres toward third-party colocation facilities to gain scalability, efficient interconnection with their required ecosystem, operational efficiency and access to specialised talent. This shift is being further accelerated by distributed AI, hybrid multi-cloud architectures and a growing focus on sustainability. ... India’s Data Centre market is distinctive because of the scale of its digital consumption, combined with the early stage of ecosystem development. India generates a significant share of global data, yet its installed data centre capacity remains comparatively low, creating strong long-term growth potential. This growth is now being amplified by hyperscalers and AI-led demand. India aims to become a USD 1 T digital economy by 2028. It is already making significant progress, supported by the country’s thriving startup ecosystem, the third largest in the world, and initiatives like Startup India.

Surprise! The One Being Ripped Off by Your AI Agent Is You

It’s now happening all the time: in the sale of location data and browsing histories to brokers who assemble and sell our highly personal profiles, and in DOGE’s and other data grabs across the federal government, where housing, tax, and health information is being weaponized for immigration enforcement or misleading voter fraud “investigations.” With AI agents, it just gets worse. Data betrayal is an even more intimate act. Yet the people who granted OpenClaw access to their accounts were making a reasonable choice—to use a powerful tool on their behalf. ... The data aggregation capabilities of AI add another dimension of risk that rarely gets even a mention, but represent a change in scale that adds up to a sea change, making someone marketed as “productivity” software a menacing vector for data weaponization. The same capabilities that make agents useful—synthesizing enormous amounts of information across sources and acting autonomously across platforms with persistence and memory—make them extraordinarily powerful instruments for state surveillance and targeted repression. An autocratic government could build dossiers on dissidents, journalists, or voters from financial records, social media, location data, and communications metadata, acting in real time: micro-targeting people with persuasion campaigns, swarming targets with coordinated social media attacks, engineering entrapment schemes, or flagging individuals based on patterns no court ever authorized.

What makes Non-Human Identities in AI secure

By aligning security goals with technological advancements, NHIs offer a tangible solution to the challenges posed by AI and cloud-based architectures. Forward-thinking organizations are leveraging this strategic advantage to stay ahead of potential threats, ensuring that their digital remain both protected and resilient. ... Can businesses effectively integrate Non-Human Identities across diverse sectors? Where industries such as financial services, healthcare, and travel become increasingly dependent on digital transformation, the need for securing NHIs is paramount. Each sector presents unique challenges and requirements that necessitate tailored approaches to NHI management. In financial services, for example, the emphasis might be on protecting transactional data, while healthcare organizations focus on safeguarding patient information. Thus, versatile solutions that accommodate varying security demands while maintaining robust protection standards are essential. ... What greater role can NHIs play where emerging technologies unfold? The growing intersection of AI and IoT devices creates a complex web of interactions that requires robust security measures. Non-Human Identities provide a framework for securely managing the myriad connections and transactions occurring between devices. In IoT networks, NHIs authenticate and authorize communication between endpoints, thus safeguarding the integrity of both data and operations.

Daily Tech Digest - August 28, 2025

Quote for the day:

“Rarely have I seen a situation where doing less than the other guy is a good strategy.” -- Jimmy Spithill

Emerging Infrastructure Transformations in AI Adoption

Balanced scaling of infrastructure storage and compute clusters optimizes resource use in the face of emerging elastic use cases. Throughput, latency, scalability, and resiliency are key metrics for measuring storage performance. Scaling storage with demand for AI solutions without contributing to technical debt is a careful balance to contemplate for infrastructure transformations. ... Data governance in AI extends beyond traditional access control. ML workflows have additional governance tasks such as lineage tracking, role-based permissions for model modification, and policy enforcement over how data is labeled, versioned, and reused. This includes dataset documentation, drift tracking, and LLM-specific controls over prompt inputs and generated outputs. Governance frameworks that support continuous learning cycles are more valuable: Every inference and user correction can become training data. ... As models become more stateful and retain context over time, pipelines must support real-time, memory-intensive operations. Even Apache Spark documentation hints at future support for stateful algorithms (models that maintain internal memory of past interactions), reflecting a broader industry trend. AI workflows are moving toward stateful "agent" models that can handle ongoing, contextual tasks rather than stateless, single-pass processing.

The rise of the creative cybercriminal: Leveraging data visibility to combat them

In response to the evolving cyber threats faced by organisations and governments, a comprehensive approach that addresses both the human factor and their IT systems is essential. Employee training in cybersecurity best practices, such as adopting a zero-trust approach and maintaining heightened vigilance against potential threats, like social engineering attacks, are crucial. Similarly, cybersecurity analysts and Security Operations Centres (SOCs) play a pivotal role by utilising Security Information and Event Management (SIEM) solutions to continuously monitor IT systems, identifying potential threats, and accelerating their investigation and response times. Given that these tasks can be labor-intensive, integrating a modern SIEM solution that harnesses generative AI (GenAI) is essential. ... By integrating GenAI's data processing capabilities with an advanced search platform, cybersecurity teams can search at scale across vast amounts of data, including unstructured data. This approach supports critical functions such as monitoring, compliance, threat detection, prevention, and incident response. With full-stack observability, or in other words, complete visibility across every layer of their technology stack, security teams can gain access to content-aware insights, and the platform can swiftly flag any suspicious activity.

How to secure digital trust amid deepfakes and AI

To ensure resilience in the shifting cybersecurity landscape, organizations should proactively adopt a hybrid fraud-prevention approach, strategically integrating AI solutions with traditional security measures to build robust, layered defenses. Ultimately, a comprehensive, adaptive, and collaborative security framework is essential for enterprises to effectively safeguard against increasingly sophisticated cyberattacks – and there are several preemptive strategies organizations must leverage to counteract threats and strengthen their security posture. ... Fraudsters are adaptive, usually leveraging both advanced methods (deepfakes and synthetic identities) and simpler techniques (password spraying and phishing) to exploit vulnerabilities. By combining AI with tools like strong and continuous authentication, behavioral analytics, and ongoing user education, organizations can build a more resilient defense system. This hybrid approach ensures that no single point of failure exposes the entire system, and that both human and machine vulnerabilities are addressed. Recent threats rely on social engineering to obtain credentials, bypass authentication, and steal sensitive data, and it is evolving along with AI. Utilizing real-time verification techniques, such as liveness detection, can reliably distinguish between legitimate users and deepfake impersonators.

Why Generative AI's Future Isn't in the Cloud

Instead of telling customers they needed to bring their data to the AI in the cloud, we decided to bring AI to the data where it's created or resides, locally on-premises or at the edge. We flipped the model by bringing intelligence to the edge, making it self-contained, secure and ready to operate with zero dependency on the cloud. That's not just a performance advantage in terms of latency, but in defense and sensitive use cases, it's a requirement. ... The cloud has driven incredible innovation, but it's created a monoculture in how we think about deploying AI. When your entire stack depends on centralized compute and constant connectivity, you're inherently vulnerable to outages, latency, bandwidth constraints, and, in defense scenarios, active adversary disruption. The blind spot is that this fragility is invisible until it fails, and by then the cost of that failure can be enormous. We're proving that edge-first AI isn't just a defense-sector niche, it's a resilience model every enterprise should be thinking about. ... The line between commercial and military use of AI is blurring fast. As a company operating in this space, how do you navigate the dual-use nature of your tech responsibly? We consider ourselves a dual-use defense technology company and we also have enterprise customers. Being dual use actually helps us build better products for the military because our products are also tested and validated by commercial customers and partners.

Why DEI Won't Die: The Benefits of a Diverse IT Workforce

For technology teams, diversity is a strategic imperative that drives better business outcomes. In IT, diverse leadership teams generate 19% more revenue from innovation, solve complex problems faster, and design products that better serve global markets — driving stronger adoption, retention of top talent, and a sustained competitive edge. Zoya Schaller, director of cybersecurity compliance at Keeper Security, says that when a team brings together people with different life experiences, they naturally approach challenges from unique perspectives. ... Common missteps, according to Ellis, include over-focusing on meeting diversity hiring targets without addressing the retention, development, and advancement of underrepresented technologists. "Crafting overly broad or tokenistic job descriptions can fail to resonate with specific tech talent communities," she says. "Don't treat DEI as an HR-only initiative but rather embed it into engineering and leadership accountability." Schaller cautions that bias often shows up in subtle ways — how résumés are reviewed, who is selected for interviews, or even what it means to be a "culture fit." ... Leaders should be active champions of inclusivity, as it is an ongoing commitment that requires consistent action and reinforcement from the top.

The Future of Software Is Not Just Faster Code - It's Smarter Organizations

Using AI effectively doesn't just mean handing over tasks. It requires developers to work alongside AI tools in a more thoughtful way — understanding how to write structured prompts, evaluate AI-generated results and iterate them based on context. This partnership is being pushed even further with agentic AI. Agentic systems can break a goal into smaller steps, decide the best order to tackle them, tap into multiple tools or models, and adapt in real time without constant human direction. For developers, this means AI can do more than suggesting code. It can act like a junior teammate who can design, implement, test and refine features on its own. ... But while these tools are powerful, they're not foolproof. Like other AI applications, their value depends on how well they're implemented, tuned and interpreted. That's where AI-literate developers come in. It's not enough to simply plug in a tool and expect it to catch every threat. Developers need to understand how to fine-tune these systems to their specific environments — configuring scanning parameters to align with their architecture, training models to recognize application-specific risks and adjusting thresholds to reduce noise without missing critical issues. ... However, the real challenge isn't just finding AI talent, its reorganizing teams to get the most out of AI's capabilities.

Industrial Copilots: From Assistants to Essential Team Members

Behind the scenes, industrial copilots are supported by a technical stack that includes predictive analytics, real-time data integration, and cross-platform interoperability. These assistants do more than just respond — they help automate code generation, validate engineering logic, and reduce the burden of repetitive tasks. In doing so, they enable faster deployment of production systems while improving the quality and efficiency of engineering work. Despite these advances, several challenges remain. Data remains the bedrock of effective copilots, yet many workers on the shop floor are still not accustomed to working with data directly. Upskilling and improving data literacy among frontline staff is critical. Additionally, industrial companies are learning that while not all problems need AI, AI absolutely needs high-quality data to function well. An important lesson shared during Siemens’ AI with Purpose Summit was the importance of a data classification framework. To ensure copilots have access to usable data without risking intellectual property or compliance violations, one company adopted a color-coded approach: white for synthetic data (freely usable), green for uncritical data (approval required), yellow for sensitive information, and red for internal IP (restricted to internal use only).

Will the future be Consolidated Platforms or Expanding Niches?

Ramprakash Ramamoorthy believes enterprise SaaS is already making moves in consolidation. “The initial stage of a hype cycle includes features disguised as products and products disguised as companies. Well we are past that, many of these organizations that delivered a single product have to go through either vertical integration or sell out. In fact a lot of companies are mimicking those single-product features natively on large platforms.” Ramamoorthy says he also feels AI model providers will develop into enterprise SaaS organizations themselves as they continue to capture the value proposition of user data and usage signals for SaaS providers. This is why Zoho built their own AI backbone—to keep pace with competitive offerings and to maintain independence. On the subject of vibe-code and low-code tools, Ramamoorthy seems quite clear-eyed about their suitability for mass-market production. “Vibe-code can accelerate you from 0 to 1 faster, but particularly with the increase in governance and privacy, you need additional rigor. For example, in India, we have started to see compliance as a framework.” In terms of the best generative tools today, he observes “Anytime I see a UI or content generated by AI—I can immediately recognize the quality that is just not there yet.”

Beyond the Prompt: Building Trustworthy Agent Systems

While a basic LLM call responds statically to a single prompt, an agent system plans. It breaks down a high-level goal into subtasks, decides on tools or data needed, executes steps, evaluates outcomes, and iterates – potentially over long timeframes and with autonomy. This dynamism unlocks immense potential but can introduce new layers of complexity and security risk. ... Technology controls are vital but not comprehensive. That’s because the most sophisticated agent system can be undermined by human error or manipulation. This is where principles of human risk management become critical. Humans are often the weakest link. How does this play out with agents? Agents should operate with clear visibility. Log every step, every decision point, every data access. Build dashboards showing the agent’s “thought process” and actions. Enable safe interruption points. Humans must be able to audit, understand, and stop the agent when necessary. ... The allure of agentic AI is undeniable. The promise of automating complex workflows, unlocking insights, and boosting productivity is real. But realizing this potential without introducing unacceptable risk requires moving beyond experimentation into disciplined engineering. It means architecting systems with context, security, and human oversight at their core.

Where security, DevOps, and data science finally meet on AI strategy

The key is to define isolation requirements upfront and then optimize aggressively within those constraints. Make the business trade-offs explicit and measurable. When teams try to optimize first and secure second, they usually have to redo everything. However, when they establish their security boundaries, the optimization work becomes more focused and effective. ... The intersection with cost controls is immediate. You need visibility into whether your GPU resources are being utilized or just sitting idle. We’ve seen companies waste a significant portion of their budget on GPUs because they’ve never been appropriately monitored or because they are only utilized for short bursts, which makes it complex to optimize. ... Observability also helps you understand the difference between training workloads running on 100% utilization and inference workloads, where buffer capacity is needed for response times. ... From a security perspective, the very reason teams can get away with hoarding is the reason there may be security concerns. AI initiatives are often extremely high priority, where the ends justify the means. This often makes cost control an afterthought, and the same dynamic can also cause other enterprise controls to be more lax as innovation and time to market dominate.

Daily Tech Digest - February 17, 2025

Quote for the day:

"Hardships often prepare ordinary people for an extraordinary destiny." -- C.S. Lewis

Like it or not, AI is learning how to influence you

We need to consider the psychological impact that will occur when we humans start to believe that the AI agents giving us advice are smarter than us on nearly every front. When AI achieves a perceived state of “cognitive supremacy” with respect to the average person, it will likely cause us to blindly accept its guidance rather than using our own critical thinking. This deference to a perceived superior intelligence (whether truly superior or not) will make agent manipulation that much easier to deploy. I am not a fan of overly aggressive regulation, but we need smart, narrow restrictions on AI to avoid superhuman manipulation by conversational agents. Without protections, these agents will convince us to buy things we don’t need, believe things that are untrue and accept things that are not in our best interest. It’s easy to tell yourself you won’t be susceptible, but with AI optimizing every word they say to us, it is likely we will all be outmatched. One solution is to ban AI agents from establishing feedback loops in which they optimize their persuasiveness by analyzing our reactions and repeatedly adjusting their tactics. In addition, AI agents should be required to inform you of their objectives. If their goal is to convince you to buy a car, vote for a politician or pressure your family doctor for a new medication, those objectives should be stated up front.

Leveraging AI for Business Continuity and Disaster Recovery in the Work-From-Home Era

AI-driven tools can monitor the health and performance of hardware and predict hardware failure before it happens using anomaly detection algorithms. For example, if a hard drive is starting to fail or there’s unusual network activity, AI systems can flag the activity/potential problem early and send an email to alert the WFH user or corporate IT staff, allowing businesses to take preventative action. ... AI can detect anomalies in network traffic or access patterns which may indicate a cyberattack (e.g., ransomware, phishing, or data breach). AI-powered cybersecurity tools, such as intrusion detection systems (IDS) and endpoint protection software, can respond automatically to threats by isolating affected systems or rolling back malicious changes. ... Small businesses may not have reliable or frequent data backups or rely on manual processes (e.g., external hard drives) that aren’t automated or secure. It may be difficult to recover without a proper backup strategy if critical data is lost due to hardware failure, cyber-attacks, or natural disasters. ... AI-assisted BC and DR solutions offer a range of benefits, particularly for SOHO and WFH users. These offerings are becoming essential as businesses of all sizes seek to maintain operational resilience in an ever-changing technological landscape.

GenAI can make us dumber — even while boosting efficiency

“A key irony of automation is that by mechanizing routine tasks and leaving exception-handling to the human user, you deprive the user of the routine opportunities to practice their judgement and strengthen their cognitive musculature, leaving them atrophied and unprepared when the exceptions do arise,” the study found. Overall, workers’ confidence in genAI’s abilities correlates with less effort in critical thinking. The focus of critical thinking shifts from gathering information to verifying it, from problem-solving to integrating AI responses, and from executing tasks to overseeing them. The study suggests that genAI tools should be designed to better support critical thinking by addressing workers’ awareness, motivation, and ability barriers. ... As Agentic AI becomes common, people may come to rely on it for problem-solving — but how will we know it’s doing things correctly, Gold said. People might accept its results without questioning, potentially limiting their own skills development by allowing technology to handle tasks. Lev Tankelevitch, a senior researcher with Microsoft Research, said not all genAI use is bad. He said there’s clear evidence in education that it can enhance critical thinking and learning outcomes.

How to harness APIs and AI for intelligent automation

APIs are the steady bridges connecting diverse systems and data sources. This reliable technology, which emerged in the 1960s and matured during the noughties ecommerce boom, is bridging today’s next-gen technologies. APIs allow data transfer to be automated, which is essential for training AI models efficiently. Rather than building complex integrations from scratch, they standardize data flow to ensure the data that feeds AI models is accurate and reliable. ... Data preprocessing is the critical step before training any AI model. APIs can ensure that AI applications and models only receive preprocessed data. This minimizes manual errors which smoothes the AI training pipeline. With a direct interface to standardized data, developers can focus on refining the model architecture rather than spending excessive time on data cleanup. Real-time evaluation keeps AI models in check in dynamic environments. By feeding real-time performance data back into the system, developers can quickly adjust parameters to improve the model. ... As your data volumes and transaction rates increase, your APIs must scale accordingly. Performance issues like latency or downtime can disrupt AI training and real-time processing. To be responsive under heavy loads, design APIs with load balancing, caching, and built-in redundancy to maintain consistent performance during peak use.

Applying Behavioral Economics to Phishing and Social Engineering Attacks

It’s all about deeply and thoroughly understanding human behavior and how these behaviors are impacted by influences that use cognitive biases, emotions, social influences, and contextual factors to drive decisions. Bad actors in the world of cybersecurity also prey upon these human tendencies to drive actions that put organizations at risk. ... Humans are social creatures that trust those they believe are authorities. They’re driven by fear, greed, and curiosity that can cloud their judgement. And they’re prone to cognitive shortcuts—biases that often drive behaviors. Understanding the power of these drivers can help organizations put strategies into place to thwart them. ... Here are some important steps that can help employees make better decisions:Training employees about the threat of cyberattacks, the form these attacks generally take, and their role in helping to avert them is an important first step. Training should be ongoing, not a single instance or once a year event. Phishing simulations have proven to be a very effective way to tangibly reduce security breakdowns. These simulations serve to test employee awareness and identify areas of opportunity for improvement. Strong authentication measures can help keep accounts secure by requiring two or more methods of identification and verification—muti-factor authentication—before allowing access to information or systems.

Why Digital Projects Need Transparency and Accountability

As a CIO, it is easy to underestimate the time it will take to build forward. In the public sector, this takes longer due to inherent risk aversion. In my first few months at DWP, I felt I was making a difference, but after the first few months, the size of the prize began to take its toll and the risk factors of going forward began to set in. As CIOs, it is our role to persuade, influence and keep in mind where we are trying to get to. We landed that vision with the senior team but DWP's size and geographic spread made it harder to get the spokes of the business to hear the same story and grasp the same benefits. If I had my time again, I would spend more time with the business, less at the center and try to build momentum that was unstoppable. As I completed my first 100 days in the CIO role at Segro, one of the key takeaways from DWP was making sure the digital leadership team knew how to act together. In my new role, I am able to replicate that at a faster pace. Brand identity matters. At Segro, we are not known as the digital team, and I am striving to change that. The organization will benefit from unifying its understanding of technology, transformation and data.

Navigating Europe’s AI Code of Practice Before the Clock Runs Out

The Code of Practice for general-purpose AI demonstrates a sincere effort to get the details right. Yet, in a rush to cover every contingency, it risks overlooking the bigger picture: spurring the next generation of AI-driven breakthroughs that can speed up drug discovery, modernize public services, and let small farmers use new predictive tools for planting and harvesting. Innovation is a delicate process, especially in emerging areas like large-scale language models or real-time climate analytics. Europe possesses the scientific expertise and market size to shape a future where these tools become transformative assets in every corner of the continent. But that future hinges on how carefully policymakers, industry players, and civil society calibrate the rules. ... Europe’s AI revolution will not happen on autopilot. Real progress demands revamping processes, investing in talent, and scaling up what works. The public sector must also move faster if Europe is to modernize healthcare, education, and core government services. Tangled or rigid rules risk derailing Europe’s ambitions. Europe’s digital regulations already weigh heavily on businesses. Over the past 25 years, the number of economy-wide laws doubled, and the EU has rolled out close to 100 tech-focused laws. High-minded ideals often mix with fragmented enforcement and overlapping rules.

Seven Common Reasons Why Data Science Projects Fail

Large organizations may own hundreds of data assets spread across sprawling, multi-faceted IT infrastructures. Unless they have a detailed, continuously updated data catalog in place that tracks all of those assets – which many don’t – simply finding the data that the team needs to complete a project can present a major challenge. Here again, however, tools and techniques are available that can help. The major solution is data discovery software, which can automatically identify data resources, including those that are not documented. ... Too often, businesses decide that they want to do something with their data, but they don’t know exactly what. For example, they might establish a high-level goal like using data-derived insights to grow revenue, without determining exactly which types of revenue-related challenges they want to solve with help from data. Avoiding this pitfall is simple: You need to articulate precise deliverables and outcomes at the start of your project. There’s always room to adjust the details a bit once a project is underway, but you should know from the beginning what the overarching outcomes of the project should be. ... A final key challenge that can thwart data science project success is the failure to understand what the goals of data science are, and which methodologies and resources data science requires.

What’s changing the rules of enterprise AI adoption for IT leaders

As model costs fall and the value from AI migrates up to the application layer, enterprises are going to have even greater choice in business solutions, either from third parties or those developed inhouse. For CIOs with access to the right resources, building applications internally is now a more realistic proposition. This becomes increasingly attractive in the context of complex business processes that may be unique to enterprises. As the costs of running models fall to near zero, the ROI equation shifts dramatically. According to Forrester Research, the ability to run hyper-efficient models like DeepSeek locally on PCs opens up a new era of edge intelligence, which businesses can deploy across organizations. “The real value in AI isn’t just in building bigger models, but innovating on top of them and in implementing them efficiently,” says Devesh Mishra, president of CoreAI at digital transformation specialists Keystone. “Companies that pair foundation model advancements with deep business and operational expertise will lead the next phase of AI-driven ROI.” This deep understanding of industry verticals and their specific issues and needs will define success for many vendors as they increasingly compete with inhouse development teams.

Rowing in the Same Direction: 6 Tips for Stronger IT and Security Collaboration

Due to market dominance, many software vendors focus on Windows, but IT fleets today include a mix of Chromebooks, Linux systems and Apple devices. Security and IT teams must recognize that the weakest endpoint determines the overall defense posture. By ensuring IT and security teams are aligned on what’s in the environment, you can break down silos and work together toward shared security goals, such as zero-trust implementation. ... Security and IT teams should collaborate to ensure policies protect the overall business mission, not just the bottom line. For example, if security requires an agent to collect telemetry for advanced analysis (e.g., CrowdStrike, Halcyon, etc.), what’s the performance impact on endpoints? If the agent is running AI/ML workloads, how is it optimized for performance on XPU and non-XPU systems? IT fleet leaders care about security BUT they also demand top performance and battery life from devices. Both security and IT teams together can align solutions that offer best-in-class security without degrading fleet performance. ... Ownership in IT and security is one of the hardest challenges to solve. In many cases, responsibility over cloud workloads, applications and ephemeral systems isn’t always clearly defined.

Daily Tech Digest - January 21, 2025

AI comes alive: From bartenders to surgical aides to puppies, tomorrow’s robots are on their way

The current generation of robots face three key challenges: processing visual information quickly enough to react in real-time; understanding the subtle cues in human behavior; and adapting to unexpected changes in their environment. Most humanoid robots today are dependent on cloud computing and the resulting network latency can make simple tasks like picking up an object difficult. ... Gen AI powers spatial intelligence by helping robots map their surroundings in real-time, much like humans do, predicting how objects might move or change. Such advancements are crucial for creating autonomous humanoid robots capable of navigating complex, real-world scenarios with the adaptability and decision-making skills needed for success. While spatial intelligence relies on real-time data to build mental maps of the environment, another approach is to help the humanoid robot infer the real world from a single still image. As explained in a pre-published paper, Generative World Explorer (GenEx) uses AI to create a detailed virtual world from a single image, mimicking how humans make inferences about their surroundings. ... Beyond the purely technical obstacles, potential societal objections must be overcome.

Why some companies are backing away from the public cloud

Technical debt may be the root of many moves back to on-premise environments. "Normally this is a self-inflicted thing," Linthicum said. "They didn't refactor the applications to make them more efficient in running on the public cloud providers. So the public cloud providers, much like if we're pulling too much electricity off the grid, just hit them with huge bills to support the computational and storage needs of those under-optimized applications." Rather than spending more money to optimize or refactor applications, these same enterprises put them back on-premise, said Linthicum. Security and compliance are also an issue. Enterprises "realize that it's too expensive to remain compliant in the cloud, with data and sovereignty rules. So, they just make a decision to push it back on-premise." The perceived high costs of cloud operations "often stem from lift-and-shift migrations that in some cases didn't optimize applications for cloud environments," said Miha Kralj, global senior partner for hybrid cloud service at IBM Consulting. "These direct transfers typically maintain existing architectures that don't leverage cloud-native capabilities, resulting in inefficient resource utilization and unexpectedly high expenses." However, the solution to this problem "isn't necessarily repatriation to on-premises infrastructure," said Kralj.

7 Common Pitfalls in Data Science Projects — and How to Avoid Them

It's worth noting, too, that just because data is of low quality at the start of a project doesn't mean the project is bound to fail. There are many effective techniques for improving data quality, such as data cleansing and standardization. When projects fail, it's typically because they failed to assess data quality and improve it as needed, not because the data was so poor in quality that there was no saving it. ... There are two key stakeholders in any data science project — the IT department, which is responsible for managing data assets, and business users, who determine what the data science project should achieve. Unfortunately, poor collaboration between these groups can cause projects to fail. For example, IT departments might decide to impose access restrictions on data without consulting business users, leading to situations where the business can't actually use the data in the way it intends. Or lack of input from business stakeholders about what they want to do may cause the IT team to struggle to determine how to deliver the data resources necessary to support a project. ... A final key challenge that can thwart data science project success is the failure to understand what the goals of data science are, and which methodologies and resources data science requires.

Facial recognition for borders and travel: 2025 trends and insights

Seamless and secure border crossings are crucial for a thriving travel industry. However, border control processes that still rely on traditional manual checks pose unnecessary risks to both national security and traveler satisfaction. Slow and cumbersome identity verification conducted by humans leads to long lines and frustrated travelers. This is where biometrics come in. Biometric technologies, particularly facial recognition, are revolutionizing border security by providing a faster, more secure and more efficient approach to verifying traveler identities. As passenger volumes continue to rise globally, transportation authorities and immigration agencies quickly realize the value of onboarding facial recognition technology to streamline busy and mission-critical border crossings — helping improve throughput, reduce wait times and enhance the overall traveler experience. ... By adopting advanced facial recognition technologies, immigration authorities can: Improve traveler experience. Self-service authentication shortens wait times and delivers a satisfying, hassle-free journey. Deliver fast and reliable authentication. The entire process to authenticate an individual is now accomplished in seconds.

Enhance border security.

AI-Driven Microservices: The Future of Cloud Scalability

Even with modern auto-scaling in cloud platforms, the limitations are clear. Scaling remains largely reactive, with additional servers spinning up only after demand spikes are detected. This lag leads to temporary throttling and performance degradation. During peak times, over-provisioning results in wasted CPU and server utilization during subsequent low-traffic periods. The inadequacy of threshold-based auto-scaling becomes particularly apparent during high-traffic events like holiday sales. Engineers often find themselves on-call to handle performance issues manually, adding operational overhead and delaying service recovery. These systems lack predictive capabilities and struggle to optimize cost and performance simultaneously. ... AI offers a solution to these challenges. Through my experience with cloud-native platforms, I have seen how AI can transform scaling capabilities by incorporating predictive analytics. Instead of waiting for problems to occur, AI-driven systems can analyze historical patterns, current trends and multiple data points to anticipate resource needs in advance. This innovation has particular significance for smaller enterprises, enabling them to compete effectively with larger organizations that have traditionally dominated due to superior infrastructure capabilities.

More AI, More Problems for Software Developers in 2025

Using AI to generate code can leave users — especially more junior developers — without the context the code was written with and who it was written for, making it harder to figure out what’s gone wrong. The risk is generally higher for junior developers. Senior developers tend to have a much better awareness and quicker understanding of the code that’s generated,” Reynolds observed. “Junior developers are under a lot of pressure to get the job done. They want to move fast, and they don’t necessarily have that contextual awareness of the code change.” Without quality and governance controls — like security scans and dependency checks, and unit, systems and integration testing — deployed throughout the software development lifecycle, he warned, the wrong thing is often merged. ... Shadow IT has developers looking to engineer their way out of a problem by adopting — and often even paying for — tools that aren’t among those officially approved by their employers. Shadow AI is an extension that sees, the report found, 52% of developers using AI tools that aren’t provided by or explicitly approved by IT. It’s not like developers are behaving insubordinately. The reality is, three years into widespread adoption of generative AI, most organizations still don’t have GenAI policies.

7 top cybersecurity projects for 2025

To effectively secure AI workloads, security teams should first gain an understanding of AI use within their enterprise, as well as the data and models used to power their business. “Next, assemble a cross-functional team to assess risks and develop a comprehensive security strategy,” Ramamoorthy advises. “Following best practices and adopting a secure AI framework will help to enable a strong security foundation and ensure that when AI models are implemented, they are secure by default.” ... With a successful TPRM project, your enterprise will have a better security posture, with fewer vulnerabilities and proactive control over outside hazards, Saine says. TPRM, backed by real-time monitoring and the ability to quickly respond to developing hazards, can also ensure compliance with pertinent laws, reducing the risk of fines and legal headaches. “Compliance will also help your enterprise project credibility and dependability to clients and partners,” he says. ... When implementing trust-by-design principles with AI-powered systems, security leaders should align their goals with overall enterprise objectives while obtaining buy-in from key executives and stakeholders. Additionally, conducting thorough assessments of the development processes can help identify vulnerabilities while prioritizing remediation and controls.

The Tech Blanket: Building a Seamless Tech Ecosystem

Traditionally, organizations have built their technology strategies around “tech stacks”—discrete tools for solving specific problems. While effective in the short term, this approach often creates silos, with each department operating within its own set of platforms. Knowledge and data are trapped, preventing the organization from realizing its full potential. In 2024, many companies recognized the limitations of this approach and began prioritizing integration. This trend will deepen in 2025 as businesses build interconnected ecosystems where tools work together harmoniously. According to Deloitte, 58% of companies are shifting their focus toward integrating their platforms into unified ecosystems rather than continuing to invest in standalone tools. ... One of the biggest challenges in building a seamless tech ecosystem is ensuring that tools communicate effectively. Selecting platforms that support open APIs is essential for facilitating easy integration. Open APIs allow different systems to share data and work together, eliminating friction and enabling better collaboration. In practical terms, this means teams can pull insights from a centralized knowledge management platform into other tools, such as CRM systems or analytics dashboards, without additional manual effort. The result? A more connected organization that can move at the speed of business.

AI Poised to Deliver Value, Innovation to Software Industry in 2025

“IoT technology has created a new level of visibility into complex, live systems and enables vital insights. By providing real-time data streams for millions of devices, IoT enables them to be monitored for issues and controlled from a distance. This will lead to ever-increasing safety, security, and efficiency in their operation. Smart buildings, transportation systems, logistics networks, and countless other applications all benefit from using IoT to provide essential services at reasonable cost. ... “The demand for faster software development has become a serious industry threat, increasing code vulnerabilities and leading to avoidable security risks. This relentless development pace is unsustainable and only being accelerated by Generative AI. The more we speed up development and release cycles with GenAI and otherwise, the more code vulnerabilities are introduced, giving attackers more opportunities to execute their missions. ... “AI is poised to become a foundational business tool, joining virtualization, cloud computing, and containerization as essential layers of modern infrastructure. By 2025, startups and enterprises will routinely leverage AI for tasks like security, audits, and cost management.

AI and cybersecurity: A double-edged sword

How exactly is AI tipping the scales in favor of cybersecurity professionals? For starters, it’s revolutionizing threat detection and response. AI systems can analyze vast amounts of data in real time, identifying potential threats with speed and accuracy. Companies like CrowdStrike have documented that their AI-driven systems can detect threats in under one second. But AI’s capabilities don’t stop at detection. When it comes to incident response, AI is proving to be a game-changer. Imagine a security system that doesn’t just alert you to a threat but takes immediate action to neutralize it. That’s the potential of AI-driven automated incident response. From isolating compromised systems to blocking malicious IP addresses, AI can execute these critical tasks swiftly and without human input, dramatically reducing response times and minimizing potential damage. ... AI is not just changing the skill set required for cybersecurity professionals, it’s augmenting it for the better. The ability to work alongside AI systems, interpret their outputs, and make strategic decisions based on AI-generated insights will be paramount for both users and experts. While AI is improving at its cybersecurity capabilities, a human paired with an AI tool will outperform AI by itself ten-fold.

Quote for the day:

"Your present circumstances don’t determine where you can go; they merely determine where you start." -- Nido Qubein