Daily Tech Digest by Kannan Subbiah: spyware

Showing posts with label spyware. Show all posts

Daily Tech Digest - October 16, 2026

Quote for the day:

"Every great story on the planet happened when someone decided not to give up, but kept going no matter what." -- Spryte Loriano

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Duration: 22 mins • Perfect for listening on the go.

Why AI Confidence Is Not Validation

Artificial intelligence tools are rapidly becoming integrated into digital forensics and incident response, but these systems are growing confident in their outputs faster than human operators can verify them. Without proper validation standards, unverified AI findings can end up in critical legal or forensic reports. To address this, SANS introduced two specialized AI frameworks mapped to industry standards, emphasizing that while AI can speed up investigations, a human examiner must remain the final authority. An AI tool that provides a confident answer without showing its underlying reasoning or sources is essentially unreliable. Practitioners cannot blindly trust an AI claim just because a high confidence score is attached; they must independently confirm the source data. Furthermore, different disciplines require entirely different policies. Incident response teams prioritize speed to detect and contain threats, whereas digital forensics demands strict validation of every file, often making AI use more labor-intensive. Ultimately, the effectiveness of AI depends heavily on the foundational skills of the human practitioner. AI should never automatically execute actions that cannot be reversed, such as collecting evidence or accessing cloud accounts without explicit authority. An inexperienced person relying solely on AI presents a significant risk to the integrity of any investigation.

CISO Conversations: James Robinson – Many Jobs, One Journey to Netskope CISO

James Robinson’s path to becoming the Chief Information Security Officer at Netskope reveals that a successful career in cybersecurity does not always follow a traditional or planned route. Initially focused more on sports than academics, Robinson discovered his passion for networking through a vocational program while in high school. This early exposure led him to acquire advanced certifications and eventually land at State Farm Insurance. When the dot-com crash disrupted his plans to join Cisco, he pivoted into the emerging field of information security, moving through roles at several major organizations and continually advancing his expertise. Throughout his journey, Robinson has relied heavily on the guidance of mentors and emphasizes the importance of building strong professional relationships. He believes that continuous learning, genuine curiosity, and enjoying the process are far more important than just reaching a specific destination or focusing solely on salary. As a leader, his primary focus is on cultivating a supportive team culture and ensuring his team members feel recognized and connected. When hiring, he values persistence, dedication, and the right mindset over formal degrees, proving that resilience and a willingness to put in the hard work are the true cornerstones of security leadership.

No click required: How the ad industry became a pipeline for mercenary spyware

Researchers from The Citizen Lab have discovered that the online advertising ecosystem is being used to deliver zero-click spyware to targeted individuals without requiring them to click on anything. A company known as Discover-Tech successfully posed as a legitimate advertising vendor, gaining approval from major ad networks like Google and IAB Europe. By participating in real-time automated ad auctions, the company accessed detailed user data, such as physical locations and device identifiers, allowing them to precisely profile millions of people worldwide. When Discover-Tech won a bid to display an ad, it mostly delivered harmless banners, which allowed it to pass routine security verification scanners. However, for specific targets, such as journalists at Al Jazeera, the ad silently delivered malicious code in the background. Because the ad auction system automatically shares user data to determine targeting, bad actors can track individuals across various networks undetected. To defend against this invisible threat, security experts advise organizations to implement strict ad-blocking policies at the network level rather than relying on users themselves. They also strongly recommend disabling mobile advertising identifiers on corporate devices and treating all ad networks as untrusted vendors, since every digital ad essentially runs external code on a user's device.

Governance Is Failing: Why Converged Digital Risk Is Outpacing Every Control We Have

Organizations today are struggling to manage modern digital threats because their governance models are outdated. Traditional structures treat cybersecurity, privacy, artificial intelligence, and compliance as separate issues managed by different departments. However, modern risks no longer stay in these neat categories. A single incident, such as a compromised identity, can instantly trigger a massive chain reaction that causes data leaks, disrupts supply chains, corrupts artificial intelligence systems, and results in severe regulatory and reputational damage. This interconnected system of threats is known as Converged Digital Risk, and it moves much faster than isolated governance frameworks can handle. To solve this, businesses must stop treating risk in silos and start viewing it as a unified system. This means shifting focus from merely protecting internal assets to understanding the massive dependencies organizations have on external tools, like cloud providers and third-party models. Ultimately, managing this complexity might require the creation of a new executive role—a Digital Risk Officer—who can oversee the entire landscape, ensure unified visibility, and make rapid decisions when multiple risk areas collide. The organizations that thrive will be those that prioritize system-wide resilience and act quickly before failures cascade.

AI Slop Isn't Just for Social Media; It's Ruining Cyberattacks, Too

Despite the alarmist headlines claiming artificial intelligence is supercharging cyber threats, security researchers are finding that AI is actually making many attackers worse at their jobs. While threat actors are using AI to scale their operations and generate content, the result is often low quality output that exposes their campaigns. Previously sophisticated cybercrime groups and even state funded operations are adopting AI to write code, design phishing lures, and create malware, but these efforts frequently backfire. The attacks are riddled with obvious mistakes, such as broken QR codes, blank emails, mismatched languages, and logical errors in malware that prevent it from functioning properly. Some groups are even exposing their own infrastructure by accidentally leaving their AI generated control panels open to researchers. Ultimately, top tier espionage and hacking operations avoid relying heavily on AI because it is too noisy and unpredictable, which dramatically increases their chances of being caught. Instead of a new era of highly advanced artificial intelligence breaches, the industry is mostly seeing a flood of sloppy, poorly executed attacks. The consensus among researchers is that the most dangerous and effective cyber threats still rely on traditional methods, making the current narrative around automated attacks largely exaggerated and unsupported by real world data.

IT reinvention requires mastering the art of translation

Technology leaders must act as translators who can clearly explain how technical systems directly benefit their companies. Rather than discussing complicated cloud architecture or artificial intelligence setups, a successful executive links technological investments to business outcomes like profitability, risk reduction, and an improved experience for customers. This approach is evident in the transformation of Deluxe Corporation, a business that successfully transitioned from a legacy check manufacturer into a modern digital payments and data company. By framing its strategy around how technology powers profitable growth, the company aligned its entire operation with clear goals like building modern platforms, using artificial intelligence responsibly, and strengthening security. True organizational reinvention requires completely changing processes, talent, and mindsets rather than just adding digital tools to old workflows. Leaders must also learn to separate practical signals from the endless noise surrounding emerging trends. Instead of chasing every new technological development, they should focus on core business functions that rarely change while tracking how shifts in areas like cybersecurity, agentic commerce, and employee roles affect those functions. By continuously learning, challenging their own assumptions, and pressure-testing ideas with peers, technology leaders can guide their organizations through continuous reinvention with confidence.

How Should Security Be Architected for Human Error?

When architecting enterprise cybersecurity, organizations must acknowledge that human error is inevitable and stop building programs that expect perfect behavior. Attackers actively manipulate employees into making mistakes, so relying solely on human judgment or even automated systems acting alone is insufficient. Instead, security programs should blend human oversight with automation, allowing each to audit the other. Security should be treated as an operational capability focused on resilience rather than a mere product feature. Instead of adopting a heavy-handed approach that completely blocks access until users complain, organizations need intelligent "default deny" or allow-listing strategies that preserve usability. Assuming that a malicious link will eventually be clicked, the focus must shift to minimizing the blast radius. This requires aggressive segmentation, strict minimization of privileges, and inline rate limits that act as "speed bumps." For example, systems should flag and pause actions that fall outside the norm, like a user attempting to delete millions of database rows or send tens of thousands of emails simultaneously. Ultimately, companies should routinely practice for failures through tabletop exercises and chaos engineering. By putting guardrails in place and testing recovery protocols before prevention fails, businesses can ensure that a single human mistake doesn't escalate into a massive crisis.

8 insights from LogicMonitor’s Elevate event: AI-driven observability moves toward autonomous IT

At the recent LogicMonitor Elevate event, the focus was on how artificial intelligence is shifting IT monitoring from passive observation to active, autonomous problem-solving. As business infrastructure becomes more complex, traditional alerts and dashboards are no longer enough. To manage this, LogicMonitor is emphasizing AI-driven observability that combines performance data with automated actions and strict governance. The results are significant: GlobalFoundries, for example, used LogicMonitor’s Edwin AI to reduce distracting alert noise by 98% in just six months, allowing them to spot potential failures in their semiconductor manufacturing before they disrupt production. A central theme of the event was the need to build trust in these automated systems. Before organizations let artificial intelligence resolve issues automatically, they need to know the AI operates within strict rules and understands the context of the business. By connecting operational data with clear guardrails, LogicMonitor aims to help IT teams resolve incidents faster and reduce the number of tools they need to manage. Ultimately, the goal is to free up engineering teams to focus on higher-value work, moving operations from simply fixing problems to actively preventing them.

Why Chief Data Officers Need to Go Beyond Data Catalogs to Deliver Value

Chief Data Officers are finding that traditional data catalogs are no longer enough to meet the demands of modern business, especially as artificial intelligence adoption increases. While companies collect vast amounts of data, it often remains locked in departmental silos or stuck in technical tools that everyday employees cannot easily use. This lack of accessible, high-quality information not only frustrates staff but also limits the effectiveness of artificial intelligence models, which require reliable inputs to function properly. To solve this, organizations must shift toward creating data products—packaged, business-focused assets designed for specific needs. However, these products only deliver value if people can easily find and use them. This is driving the adoption of data marketplaces, which function much like e-commerce storefronts, allowing non-technical users to search for, understand, and access reliable information through intuitive interfaces. A successful transition requires more than just new technology; companies must foster a culture of data literacy and trust by ensuring the experience is straightforward and the initial offerings are highly relevant. By moving past reactive data catalogs and embracing user-friendly marketplaces, businesses can effectively share information across teams and artificial intelligence systems, turning their data into tangible, long-term value.

Developers Positive About AI - but Trust Remains Conditional

According to Stack Overflow’s 2026 Developer Survey, while AI adoption in the workplace has reached near saturation, developers remain deeply skeptical of its output. Six in ten developers view artificial intelligence favorably, yet nearly half only trust the technology when they can independently verify its work. A mere seven percent trust AI for critical decisions, highlighting a strong demand for transparency, technical context, and clear source attribution. Most developers actively use coding assistants and find them highly effective for implementation, debugging, and refactoring, but their perceived utility drops significantly for human oriented tasks like design and team communication. Furthermore, organizational governance is lagging, with many companies lacking official policies for AI use. Beyond AI, the survey paints a complex picture of the modern workplace. Almost half of developers feel complacent in their current roles, largely driven by burnout, unclear priorities, and tech fatigue. Despite a push for return to office mandates, remote work remains the preferred arrangement. In terms of technology, familiar foundations like JavaScript and Python continue to dominate the landscape. Interestingly, while OpenAI and Anthropic currently see equal usage among developers, there is a growing preference to use Anthropic models and agents in the coming year.

Daily Tech Digest - September 29, 2026

Quote for the day:

"We don't grow when things are easy. We grow when we face challenges." -- Elizbeth McCormick

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Duration: 18 mins • Perfect for listening on the go.

Nine unlikely trends shaping software development

The software development landscape is experiencing a surprising shift where older, foundational technologies are re-emerging to overtake modern trends. According to InfoWorld, nine unexpected reversals are currently shaping the industry. Plain JavaScript is moving to absorb TypeScript, transforming the latter into a simple linting tool rather than a mandatory compilation step. Similarly, SQL is seeing a strong resurgence over ORMs and NoSQL databases, valued for its rigorous structure and new capabilities like running in the browser via WebAssembly. Developers are also finding that local IDEs often outperform cloud development environments due to the sheer power of modern laptops. In system architecture, monolithic designs are beating out microservices, as teams realize the deep complexities and network latency of microservices are often unnecessary for their goals. Instead of complex API integrations, developers are embracing cohesive "batteries-included" frameworks that reduce brittle glue code. We are also seeing a shift back to on-premises hardware over default cloud deployments, a preference for specialized engineering roles over the myth of the true full-stack developer, WebAssembly challenging Docker with faster, lightweight portability, and Java reclaiming dominance on the server side thanks to highly scalable virtual threads.

Beyond redundancy: Why dynamic stability matters in AI data centers

As artificial intelligence transforms data centers, the traditional approach to facility resilience is no longer enough. The challenge has shifted from static redundancy to dynamic stability. In conventional computing setups, uninterruptible power supplies and backup generators act as insurance against hardware failure. However, massive clusters of AI accelerators can change their power demand in milliseconds during training cycles. These tightly synchronized shifts create massive, instant power transitions without any actual equipment failing. Because thousands of GPUs can jump from low to full power demand almost instantly, they stress the entire electrical system. Utilities, grid researchers, and infrastructure companies are now focusing on active control to keep generators, batteries, and the grid synchronized during these sudden load changes. Modern power systems are being reimagined as dynamic buffers rather than just emergency backups, utilizing advanced firmware to absorb rapid power spikes without constantly cycling and degrading batteries. Ultimately, it is not enough for an AI data center to merely survive a localized power loss event. Operators must actively manage how the entire electrical infrastructure behaves millisecond by millisecond, ensuring the facility remains fully stable and completely responsive to the extreme, repetitive power swings of heavy AI workloads.

The human-on-the-loop advantage for MSSPs

Artificial intelligence is quickly changing how Managed Security Service Providers (MSSPs) operate, offering the ability to analyze data, automate workflows, and accelerate investigations at speeds humans cannot match. MSSPs face growing pressures—including skills shortages, complex attack surfaces, and tight budgets—making AI a crucial tool for scaling operations. However, despite the rise of automated security, AI does not eliminate the need for skilled cybersecurity professionals. Instead, it shifts the focus to a "human-on-the-loop" model, where analysts no longer perform every task manually but set guardrails, review high-risk decisions, and step in during complex incidents. AI excels at finding patterns and reducing noise, but it lacks the contextual understanding and nuanced judgment required to navigate ambiguous, real-world security threats. Furthermore, as attackers increasingly use AI-enabled techniques like prompt injection and model exploitation, AI systems themselves have become part of the attack surface. This makes human oversight essential to validate findings and challenge automated decisions. Ultimately, the most successful MSSPs will be those that blend AI-driven efficiency with adaptable, highly trained professionals who know when to trust the technology and when to override it.

IT Service Operations Is Ready For Its AI Moment

IT service operations are stepping into a new era where artificial intelligence finally moves from theory to practical application. For years, service desks and IT operations teams have struggled with a growing volume of routine requests, endless alerts, and the constant pressure to resolve issues faster. Now, the integration of artificial intelligence is offering a reliable way to shift from a reactive approach to a more proactive model. By applying modern AI tools, organizations can automate the categorization and routing of support tickets, significantly reducing the manual effort required from IT staff. Furthermore, intelligent virtual agents and improved self-service portals provide employees with immediate answers to common problems, creating a smoother and more efficient experience for everyone involved. For more complex incidents, AI assists support teams by quickly summarizing historical data and suggesting potential fixes, which directly cuts down the time it takes to restore normal operations. However, achieving this transition requires more than just buying new software. Technology leaders must focus on organizing their underlying data and refining their existing service workflows. When executed thoughtfully, adopting AI in service operations frees up technical teams to focus on strategic projects rather than getting bogged down by repetitive troubleshooting.

7 reasons IT managers fail to exceed your expectations

Many IT managers fail to meet or exceed expectations despite having strong technical backgrounds, often because the role requires skills they haven't developed. According to industry experts, the transition from a top-performing individual contributor to a manager requires critical thinking, business understanding, and leadership—areas where technical training falls short. Seven core reasons outline why IT managers often struggle in their roles. First, many are promoted without formal management training, leaving them ill-equipped to guide teams. They may also lack the emotional intelligence and interpersonal skills necessary to handle complex situations. Additionally, an individual might simply be the wrong fit for a specific management position, or they may lack clear expectations and performance metrics from their own supervisors. Sometimes, professionals take management roles just to advance their careers, even if they prefer staying technical. When they do take the role, they often juggle too many responsibilities without clear prioritization from the CIO, making it hard to stay on track. Finally, struggling managers often focus purely on flawless technology execution rather than solving the actual business problems at hand. CIOs can fix these issues by offering mentorship, establishing technical career tracks, and setting clear, business-driven goals.

Background Check Fraud: What Screening Can Miss

It is a troubling reality for security and human resources leaders that every fraudulent employee discovered by experts had successfully passed a standard background check. This vulnerability is not a flaw in the background checks themselves, which simply answer a narrow question by confirming that records exist, documents are legitimate, and names match database entries. Instead, the issue lies in the widening identity gap that has become an enormous business risk, costing companies hundreds of millions of dollars. Bad actors can now easily steal real identities, build convincing personas, optimize resumes for automated screeners, and even use generative artificial intelligence to navigate video interviews. Because traditional screening systems are not designed to compare a person's claimed history against independent sources, they fail to reveal inconsistencies in a broader digital footprint. A fabricated persona often appears legitimate if the underlying documents check out. To combat this growing threat, organizations must adopt a strategy of ongoing identity corroboration throughout the entire employment lifecycle. This broader approach focuses on ensuring that an individual is consistent, traceable, and genuine across multiple independent sources, shifting the focus from merely asking if a document is real to verifying if the person actually is who they claim to be.

Stolen AI credentials feed growing LLM proxy economy

Threat actors are increasingly utilizing over 80,000 proxy servers, known as transfer stations, to cloak illicit traffic to frontier AI models. This growing underground economy relies on stolen AI subscription credentials and API keys, which are often harvested through information stealers, phishing campaigns, and supply chain attacks targeting privileged developer accounts. By hiding the geographic origin of their traffic, attackers bypass provider controls to conduct model distillation attacks. In these attacks, carefully designed prompts extract valuable knowledge from top tier models to train competing AI systems. Security researchers have traced a significant portion of this activity to IP addresses in China and Hong Kong, echoing recent warnings from federal agencies about industrial scale distillation efforts. Beyond distillation, these proxy networks fuel widespread AI token theft, leading to hundreds of thousands of dollars in financial losses for victimized organizations. The proxies are often powered by open source relay platforms like sub2api, supported by a surprisingly robust commercial ecosystem of resellers and proxy vendors. To combat this rising threat, security experts strongly advise organizations to treat AI credentials as critical production secrets. Enterprises should implement short lived tokens, enforce strict spending limits, monitor for unusual request volumes, and quickly revoke any compromised keys.

AI Resilience: As AI Gets Smarter, Are Humans Still Getting Better?

As organizations shift toward more autonomous AI systems that reason and act, a critical new risk is emerging: cognitive dependency. While traditional AI governance focuses on machine accuracy and safety, there is growing concern about what happens to human capability when critical thinking is heavily delegated to technology. Offloading complex tasks like analysis and decision-making creates an efficiency paradox where enormous productivity gains might lead to gradual cognitive atrophy in human workers. To counter this, meaningful oversight must go beyond merely having a "human in the loop" who passively clicks approval buttons. True oversight requires a "human at the helm" who retains the ability to understand context, challenge the AI's assumptions, and confidently override recommendations when necessary. This introduces the concept of "AI resilience"—the organizational imperative to ensure employees maintain their independent judgment and domain expertise alongside AI adoption. Building this resilience involves deliberate practices, such as requiring humans to formulate their own initial judgments before viewing AI outputs and conducting critical tasks independently of AI. Ultimately, the goal is not to limit artificial intelligence, but to ensure that as machines become smarter, human workers do not lose the essential critical thinking skills required to properly govern them.

Five Ways To Use AI Coding Agents to Improve Your Software Architecture

AI coding agents are becoming essential tools for improving software architecture, especially as systems grow more complex and often rely on poorly understood legacy services. Modern architectures frequently integrate older services for specific tasks, but these often lack accurate documentation, making their use risky. AI coding agents can bridge this knowledge gap by mapping system designs, documenting data flows, and identifying potential security or logic flaws within legacy code. If necessary, these agents can even refactor the code to improve maintainability and mitigate architectural risks. Beyond dealing with legacy systems, AI agents are highly effective at finding and fixing both generic and organization-specific architectural flaws, such as API design issues or Domain-Driven Design boundary violations. They are also adept at identifying and patching security vulnerabilities, which is particularly valuable when architectures incorporate open-source packages. Furthermore, while AI agents significantly speed up coding and free teams to experiment, they must be guided by specific, measurable architectural goals and trade-offs to ensure quality. By doing so, teams can rapidly generate Minimum Viable Architectures (MVAs) and evaluate the code through measurable tests, creating a solid foundation for robust, scalable, and secure systems.

Chrome Store Hosts 'Poper Blocker' Spyware Downloaded by Millions

Millions of users have unwittingly downloaded a malicious browser extension called Poper Blocker, believing it to be a legitimate ad blocker. Despite carrying Google’s "Featured" badge and "Established Publisher" status on the Chrome Web Store, researchers at Bay Area Labs identified the program as sophisticated spyware. Once installed, the extension quietly gathers extensive amounts of sensitive information. It records detailed browser histories, captures screenshots, and extracts highly specific data from AI chatbot interactions on platforms like ChatGPT and Gemini. To bypass security reviews, the software remains inactive for its first 24 hours and uses methods to avoid detection, such as hiding its code and recognizing test environments. It then communicates with an external server to execute harmful commands. The developer behind the app, an opaque company known as Big Star Labs, has previously been caught distributing similar spyware, yet several of its applications remain freely available to millions of users. Security experts warn that standard data protection tools struggle to detect this behavior because the stolen data is heavily disguised. The situation highlights a broader issue in the digital marketplace, where users have very limited ways to distinguish safe utilities from deceptive software designed to quietly monitor their private lives each day.

Daily Tech Digest - March 27, 2026

Quote for the day:

“Our greatest fear should not be of failure … but of succeeding at things in life that don’t really matter.” -- Francis Chan

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Duration: 22 mins • Perfect for listening on the go.

Digital Transformation Is Not A Technology Problem; It’s An Addition Problem

In the Forbes Tech Council article, Andrew Siemer argues that the staggering failure rate of digital transformation—with some reports suggesting up to 88% of initiatives fall short—stems from a fundamental behavioral bias known as the "." Drawing on research from the University of Virginia, Siemer explains that humans instinctively attempt to solve complex problems by adding new elements, such as additional software platforms or dashboards, rather than subtracting existing inefficiencies. This compulsion to add is particularly pronounced under cognitive load, leading companies to accumulate technical debt and complexity even as global digital transformation investments are projected to reach $4 trillion by 2028. Siemer contends that the most successful organizations are those that resist this additive instinct and instead focus on "removing work." He challenges leaders to reconsider their transformation roadmaps, which often default to implementation and replacement, and instead prioritize radical simplification. By asking what processes should be stopped rather than what technology should be started, businesses can move beyond the cycle of unsuccessful investment. Ultimately, digital transformation is not merely a technological challenge but a strategic discipline of subtraction that requires shifting focus from scaling tools to streamlining core operations.

Vendors race to build identity stack for Agentic AI

The rapid rise of , capable of executing complex tasks and financial transactions at machine speed, has triggered a competitive race among identity management vendors to develop specialized "." Traditional security frameworks, designed for human interaction and intermittent logins, are proving insufficient for managing autonomous entities that lack natural human friction. Consequently, enterprises face significant visibility and accountability gaps regarding agent activity and permissions. To address these vulnerabilities, major players like have launched dedicated frameworks such as "Identity for AI," which focuses on real-time enforcement and rather than shared human credentials. Simultaneously, firms like Wink and Vouched are integrating multimodal biometrics to anchor agent actions to verifiable human consent, particularly for scoped payment authorizations that limit transaction amounts. Other innovators, including Saviynt and Dock Labs, are introducing governance platforms and open protocols to manage agent-to-agent trust and verify intent via cryptographic credentials. By shifting enforcement to runtime and treating AI agents as a distinct identity class, these vendors aim to provide the necessary guardrails for the emerging era of agentic commerce, ensuring that autonomous systems remain securely anchored to provable human oversight and rigorous auditable standards.

Inside a Modern Fraud Attack: From Bot Signups to Account Takeovers

The article "Inside a Modern Fraud Attack: From Bot Signups to Account Takeovers" highlights the evolution of digital fraud into a sophisticated, multi-stage "relay race" that bypasses traditional security measures. These attacks typically begin with large-scale automation, utilizing bots and scripts to create numerous accounts using compromised emails and residential proxies to mimic legitimate residential traffic. As the attack progresses, fraudsters pivot from automated methods to slower, human-driven activities to blend in with normal user behavior. This tactical shift culminates in account takeovers and monetization through or phishing. The article argues that relying on single-signal defenses, such as IP reputation or email validation alone, is increasingly ineffective and prone to false positives. Instead, organizations must adopt a multi-signal correlation strategy that unifies IP intelligence, device fingerprinting, identity verification, and behavioral analytics. By evaluating these data points in context throughout the entire user journey, security teams can effectively identify coordinated abuse clusters while maintaining a low-friction experience for genuine customers. Ultimately, outpacing modern fraud requires a holistic, integrated risk model that moves beyond disconnected, point-in-time checks to address the full lifecycle of complex cyberattacks.

What IT leaders need to know about AI-fueled death fraud

AI-fueled death fraud is an emerging cybersecurity threat where criminals leverage generative AI to produce highly convincing, fake death certificates and legal documents. By faking a customer’s passing or impersonating heirs, fraudsters exploit empathetic bereavement workflows to seize control of sensitive accounts, financial assets, and personal data. This tactic is particularly dangerous because many enterprise identity systems are designed for long-term users and lack robust protocols for managing post-mortem transitions. Currently, the absence of centralized, real-time government databases for death verification creates a significant security gap that IT leaders must address. Beyond direct financial theft, attackers often use compromised accounts to launch sophisticated social engineering campaigns against the victim’s contacts. To mitigate these risks, experts suggest that IT leaders move away from simple credential-based access toward delegated authority frameworks and behavioral analytics that monitor for sudden, unexplained shifts in account activity. Furthermore, organizations should update terms of service to define digital legacy procedures. By formalizing verification processes and integrating rigorous oversight, businesses can better protect customers’ digital estates from being weaponized. This approach ensures the human element of bereavement does not become a permanent vulnerability in an increasingly automated world.

Vibe coding your own enterprise apps is edgy business

"Vibe coding," the practice of using AI agents to generate software through natural language prompts, is revolutionizing enterprise application development while introducing significant operational risks. As detailed in the CIO article, this shift enables companies to rapidly prototype and build custom internal tools—such as dashboards and workflow systems—often bypassing traditional procurement processes and expensive external agencies. While the speed and cost-effectiveness of this approach are seductive, IT leaders warn that it can quickly lead to a maintenance nightmare. Unlike road-tested SaaS platforms, vibe-coded applications place the entire burden of security, integration, and long-term support directly on the organization. Furthermore, the ease of creation risks fostering a chaotic environment of "shadow IT," where unsupervised employees generate technical debt and fragmented systems lacking robust architecture. Experts highlight a "seduction phase" where tools initially appear brilliant but later fail under the weight of production requirements or data integrity concerns. Consequently, CIOs are urged to implement strict governance, ensure human-in-the-loop oversight, and maintain a cautious distance from using experimental AI for mission-critical systems. Ultimately, vibe coding offers a powerful competitive edge for innovation, yet successful enterprise adoption requires balancing rapid creativity with disciplined engineering standards to prevent a future of unmanageable and broken software.

The CISO’s guide to responding to shadow AI

The rapid proliferation of artificial intelligence has introduced a new cybersecurity challenge known as shadow AI, where employees utilize unapproved AI tools to boost productivity. This CSO Online guide outlines a strategic four-step framework for CISOs to manage these hidden risks effectively. First, leaders must calmly assess risks by evaluating data sensitivity and potential for breaches rather than reacting impulsively. Understanding the underlying motivations for shadow AI use is the second step, as it often reveals unmet business needs or productivity gaps. Third, CISOs must decide whether to strictly block these tools or integrate them through formal vetting processes involving legal and security reviews. Finally, the article emphasizes evolving AI governance by improving employee education and creating clear pathways for tool approval. Rather than relying solely on punishment, organizations should foster a culture of accountability where responsibility for AI safety is shared across all departments. Ultimately, while shadow AI cannot be entirely eliminated, it can be mitigated through proactive management and transparent communication. By viewing these instances as opportunities to refine policy and secure additional resources, CISOs can transform shadow AI from a liability into a catalyst for secure innovation.

Why ‘Invisible AI’ is at the heart of durable value creation for enterprises

In the article "Why Invisible AI is at the Heart of Durable Value Creation for Enterprises," Ankor Rai argues that the most impactful artificial intelligence initiatives are those integrated so deeply into operational workflows that they become virtually invisible. While many organizations struggle to scale AI beyond experimental models, durable value is found when intelligence is embedded directly into the fabric of daily processes to stabilize operations and reduce friction. This "" shifts the focus from dramatic transformations to preventative success, where value is measured by the absence of failures, such as equipment downtime or stalled workflows. Rai highlights that the primary challenge is bridging the gap between insight and action; effective systems deliver real-time signals at the precise moment of decision rather than through separate reports. By automating repetitive, high-volume tasks like data reconciliation and anomaly detection, enterprises do not replace human expertise but rather protect it, allowing leadership to focus on nuanced strategy and complex problem-solving. Ultimately, the maturity of enterprise technology is evidenced by its ability to quietly improve reliability and compress error margins. This invisible integration creates a compounding competitive advantage rooted in operational resilience, consistency, and the preservation of organizational bandwidth over time.

Intermediaries Driving Global Spyware Market Expansion

The proliferation of third-party intermediaries, including resellers and exploit brokers, is significantly expanding the by undermining transparency efforts and bypassing government restrictions. According to a recent report from the Atlantic Council, these entities serve as the operational backbone of the industry, enabling both sanctioned nations and private actors to acquire advanced surveillance tools regardless of trade bans or diplomatic tensions. By muddying supply chains and obscuring the origins of offensive cyber capabilities, intermediaries allow countries with limited technical expertise to purchase sophisticated hacking software on the open market. This evolution has transformed the spyware ecosystem into a modular supply chain where commercial vendors now outpace traditional state-sponsored groups in zero-day exploit attribution. Despite international diplomatic efforts like the Pall Mall Process, regulating this "shadowy" marketplace remains difficult because the complex corporate structures of these brokers are designed specifically to make export controls irrelevant. Experts suggest that establishing "Know Your Vendor" requirements and formal certification processes for resellers are essential steps toward gaining visibility. Ultimately, the lack of transparency driven by these intermediaries continues to pose a severe threat to human rights and global security as surveillance technology spreads unchecked across borders.

Designing self-healing microservices with recovery-aware redrive frameworks

In modern cloud-native architectures, traditional retry mechanisms often exacerbate system failures by triggering "retry storms" that overwhelm recovering services. To address this, the article introduces a recovery-aware redrive framework specifically designed to create truly self-healing microservices. This framework operates through three critical stages: failure capture, health monitoring, and controlled replay execution. Initially, failed requests are persisted in durable queues with full metadata to ensure exact replay semantics. Instead of immediate retries, a monitoring function continuously evaluates downstream service health metrics, such as error rates and latency. Once recovery is confirmed, queued requests are replayed at a controlled, throttled rate to prevent further network congestion. This decoupled approach ensures that all failed requests are eventually processed while maintaining overall system stability and avoiding dangerous cascading failures. By integrating real-time health data with a gated replay mechanism, the framework enhances observability and provides a platform-agnostic solution for complex distributed systems. Ultimately, this method reduces the need for manual intervention, improves long-term reliability, and allows engineers to track recovery events with high precision, making it a vital evolution for resilient microservice design in high-scale environments where maintaining uptime is paramount.

Architectural Governance at AI Speed

In the era of generative AI, where code has become a commodity, the primary challenge for software organizations is no longer production but architectural alignment. The InfoQ article "Architectural Governance at AI Speed" argues that traditional review boards and centralized oversight can no longer scale with the sheer volume of AI-generated output. Instead, it proposes "," a model that transforms Architectural Decision Records (ADRs) and Event Models into machine-enforceable guardrails. By utilizing vertical slices—self-contained units of behavior—teams can automate code generation and validation, ensuring that the conformant path becomes the path of least resistance. A key mechanism described is the "Ralph Wiggum Loop," an AI-looping technique where agents iteratively refine implementations until they meet specific Given-When-Then criteria. This approach enables decentralized governance by allowing teams to work independently while maintaining cohesion through shared collaborative modeling. Ultimately, the shift from "dumping left" to automated, declarative systems allows human architects to move beyond policing implementation details and focus on high-level intent and product alignment. By embedding governance directly into the development lifecycle, organizations can achieve rapid delivery without sacrificing system integrity or consistency across team boundaries.

Daily Tech Digest - June 16, 2025

Quote for the day:

"A boss has the title, a leader has the people." -- Simon Sinek

How CIOs are getting data right for AI

Organizations that have taken steps to better organize their data are more likely to possess data maturity, a key attribute of companies that succeed with AI. Research firm IDC defines data maturity as the use of advanced data quality, cataloging and metadata, and data governance processes. The research firm’s Office of the CDO Survey finds firms with data maturity are far more likely than other organizations to have generative AI solutions in production. ... “We have to be mindful of what we put into public data sets,” says Yunger. With that caution in mind, Servier has built a private version of ChatGPT on Microsoft Azure to ensure that teams benefit from access to AI tools while protecting proprietary information and maintaining confidentiality. The gen AI implementation is used to speed the creation of internal documents and emails, Yunger says. In addition, personal data that might crop up in pharmaceutical trials must be treated with the utmost caution to comply with the European Union’s AI Act, ... To achieve what he calls “sustainable AI,” AES’s Reyes counsels the need to strike a delicate balance: implementing data governance, but in a way that does not disrupt work patterns. He advises making sure everyone at your company understands that data must be treated as a valuable asset: With the high stakes of AI in play, there is a strong reason it must be accurately cataloged and managed.

Alan Turing Institute reveals digital identity and DPI risks in Cyber Threats Observatory Workshop

The trend indicates that threat actors could be targeting identity mechanisms such as authentication, session management, and role-based access systems. The policy implication for governments translates to a need for more detailed cyber incident reporting across all critical sectors, the institute recommends. An issue is the “weakest link” problem. A well-resourced sector like finance might invest in strong security, but their dependence on, say, a national ID system means they are still vulnerable if that ID system is weak. The institute believes this calls for viewing DPI security as a public good. Improvements in one sector’s security, such as “hardened” digital ID protocols, could benefit other sectors’ security. Integrating security and development teams is recommended as is promoting a culture of shared cyber responsibility. Digital ID, government, healthcare, and finance must advance together on the cybersecurity maturity curve, the report says, as a weakness in one can undermine the public’s trust in all. The report also classifies CVEs by attack vectors: Network, Local, Adjacent Network, and Physical. Remote Network threats were dominant, particularly affecting finance and digital identity platforms. But Local and Physical attack surfaces, especially in health and government, are increasingly relevant due to on-premise systems and biometric interfaces, according to the Cyber Threat Observatory.

The Advantages Of Machine Learning For Large Restaurant Chains

Machine learning can not only assist in the present activities but contribute to steering long-term planning and development. Decision-makers can use these trends to notice opportunities to explore new markets, develop new products, or redistribute resources when they discover the patterns across the different locations, customer groups, and product categories. These insights dig deeper into the superficial data and reveal trends that might not have been apparent by just manual analysis. The capability to make data-driven decisions becomes even more significant with the growth of restaurant chains. Machine learning tools provide scalable insights that can be applied in parallel with the rest of the business objectives when combined with other technologies like a drive thru system or cloud-based analytics platforms. The opening of a new venue or the optimizing of an advertisement campaign, machine learning enables the management levels to have the information needed to make a decision with assured confidence and competence. ... Machine learning is transforming how major restaurant chains run their business, providing an unbeatable mix of accuracy, speed, and flexibility over their older equivalents.

How Staff+ Engineers Can Develop Strategic Thinking

For risk and innovation, you need to understand what your organization values the most. Everybody has a culture memo and a set of tenets they follow, but these are part of unsaid rules, something that every new hire will learn by the first week of their onboarding, which is not written out loud and clear. In my experience, there are different kinds of organizations. Some care about execution, like results above everything, top line, bottom line. Others care about data-driven decision-making, customer sentiment, and keeping adapting. There are others who care about storytelling and relationships. What does this really mean? If you fail to influence, if you fail to tell a story about what ideas you have, what you're really trying to do, to build trust and relationships, you may not succeed in that environment, because it's not enough for you to be smart and knowing it all. You also need to know how to convey your ideas and influence people. When you talk about innovation, there are companies that really pride themselves on experimentation, staying ahead of the curve. You can look at this by how many of them have an R&D department, and how much funding they put into that. Then, what's their role in the open-source community, and how much they contribute towards it.

Legal and Policy Responses to Spyware: A Primer

There have been a number of international efforts to combat at least some aspects of the harms of commercial spyware. These include the US-led Joint Statement on Efforts to Counter the Proliferation and Misuse of Commercial Spyware and the Pall Mall Process, an ongoing multistakeholder undertaking focussed on this issue. So far, principles, norms, and calls for businesses to comply with the United Nations Guiding Principles on Business and Human Rights (UNGPs) have emerged, and Costa Rica has called for a full moratorium, but no well-orchestrated international action has been fully brought to fruition. However, private companies and individuals, regulators, and national or regional governments have taken action, employing a wide range of legal and regulatory tools. Guidelines and proposals have also been articulated by governmental and non-governmental organizations, but we will focus here on measures that are existent and, at least in theory, enforceable. While some attempts at combating spyware, like WhatsApp’s, have been effective, others have not. Analyzing the strengths and weaknesses of each approach is beyond the scope of this article, and, considering the international nature of spyware, what fails in one jurisdiction may be successful in another.

Red Teaming AI: The Build Vs Buy Debate

In order to red team your AI model, you need to have a deep understanding of the system you are protecting. Today’s models are complex multimodal, multilingual systems. One model might take in text, images, code, and speech with any single input having the potential to break something. Attackers know this and can easily take advantage. For example, a QR code might contain an obfuscated prompt injection or a roleplay conversation might lead to ethical bypasses. This isn’t just about keywords, but about understanding how intent hides beneath layers of tokens, characters, and context. The attack surface isn’t just large, it’s effectively infinite. ... Building versus buying is an age-old debate. Fortunately, the AI security space is maturing rapidly, and organizations have a lot of choices to implement from. After you have some time to evaluate your own criteria against Microsoft, OWASP and NIST frameworks, you should have a good idea of what your biggest risks are and key success criteria. After considering risk mitigation strategies, and assuming you want to keep AI turned on, there are some open-source deployment options like Promptfoo and Llama Guard, which provide useful scaffolding for evaluating model safety. Paid platforms like Lakera, Knostic, Robust Intelligence, Noma, and Aim are pushing the edge on real-time, content-aware security for AI, each offering slightly different tradeoffs in how they offer protection.

The Impact of Quantum Decryption

There are two key quantum mechanical phenomena, superposition and entanglement, that enable qubits to operate fundamentally differently than classical bits. Superposition allows a qubit to exist in a probabilistic combination of both 0 and 1 states simultaneously, significantly increasing the amount of information a small number of qubits can hold. ... Quantum decryption of data stolen using current standards could have pervasive impacts. Government secrets, more long-term data, and intellectual property remain at significant risk even if decrypted years after a breach. Decrypted government communications, documents, or military strategies could compromise national security. An organization’s competitive advantage could be undermined by trade secrets being exposed. Meanwhile, data such as credit card information will diminish over time due to expiration dates and the issuance of new cards. ... For organizations, the ability of quantum computers to decrypt previously stolen data could result in substantial financial losses due to data breaches, corporate espionage, and potential legal liabilities. The exposure of sensitive corporate information, such as trade secrets and strategic plans, could provide competitors with an unfair advantage, leading to significant financial harm.

Don't let a crisis of confidence derail your data strategy

In an age of AI, the options that range from on-premise facilities to colocation, or public, private and hybrid clouds, are business-critical decisions. These decisions are so important because such choices impact the compliance, cost efficiency, scalability, security, and agility that can make or break a business. In the face of such high stakes, it is hardly surprising that confidence is the battleground on which deals for digital infrastructure are fought. ... Commercially, Total Cost of Ownership (TCO) has become another key factor. Public cloud was heavily promoted on the basis of lower upfront costs. However, businesses have seen the "pay-as-you-go" model lead to escalating operational expenses. In contrast, businesses have seen the cost of colocation and private cloud become more predictable and attractive for long-term investment. Some reports suggest that at scale, colocation can offer significant savings over public cloud, while private cloud can also reduce costs by eliminating hardware procurement and management. Another shift in confidence has been that public cloud no longer guarantees the easiest path to growth. Public cloud has traditionally excelled in rapid, on-demand scalability. This agility was a key driver for adoption, as businesses sought to expand quickly.

The Anti-Metrics Era of Developer Productivity

The need to measure everything truly spiked during COVID when we started working remotely, and there wasn’t a good way to understand how work was done. Part of this also stemmed from management’s insecurities about understanding what’s going on in software engineering. However, when surveyed about the usefulness of developer productivity metrics, most leaders admit that the metrics they track are not representative of developer productivity and tend to conflate productivity with experience. And now that most of the code is written by AI, measuring productivity the same way makes even less sense. If AI improves programming effort by 30%, does that mean we get 30% more productivity?” ... Whether you call it DevEx or platform engineering, the lack of friction equals happy developers, which equals productive developers. In the same survey, 63% of developers said developer experience is important for their job satisfaction. ... Instead of building shiny dashboards, engineering leads should focus on developer experience and automated workflows across the entire software development life cycle: development, code reviews, builds, tests and deployments. This means focusing on solving real developer problems instead of just pointing at the problems.

Why banks’ tech-first approach leaves governance gaps

Integration begins with governance. When cybersecurity is properly embedded in enterprise-wide governance and risk management, security leaders are naturally included in key forums, including strategy discussions, product development, and M&A decision making. Once at the table, the cybersecurity team must engage productively. They must identify risks, communicate them in business terms AND collaborate with the business to develop solutions that enable business goals while operating within defined risk appetites. The goal is to make the business successful, in a safe and secure manner. Cyber teams that focus solely on highlighting problems risk being sidelined. Leaders must ensure their teams are structured and resourced to support business goals, with appropriate roles and encouragement of creative risk mitigation approaches. ... Start by ensuring there is a regulatory management function that actively tracks and analyzes emerging requirements. These updates should be integrated into the enterprise risk management (ERM) framework and governance processes—not handled in isolation. They should be treated no differently than any other new business initiatives. ... Ultimately, aligning cyber governance with regulatory change requires cross-functional collaboration, early engagement, and integration into strategic risk processes, not just technical or compliance checklists.

Daily Tech Digest - February 06, 2024

Championing privacy-first security: Harmonizing privacy and security compliance

When security solutions are crafted with privacy as a central consideration, organisations can deploy robust security measures while safeguarding the personal data of their customers and employees. A comprehensive cost-benefit analysis reveals significant advantages in adopting a privacy-first approach to security. For instance, proactively blocking malware before it infiltrates an organisation’s systems can avert a potential data breach. Given the average cost of US$4.45 million in 2023, coupled with the consequential impact on brand reputation and legal ramifications, preventing even a single data breach becomes paramount for any company. Hence, the importance of industry-leading security measures is indisputable. Any reputable security company should provide solutions that limit its access to sensitive data and ensure the protection of the personal data entrusted to its care. ... A privacy-first security program assesses the risks associated with both implementing and not implementing security measures. If the advantages of deploying a security solution, such as email scanning, outweigh the drawbacks – which is highly probable – the organisation should proceed with the careful implementation of this capability.

Far Memory Unleashed: What Is Far Memory?

Far memory is a memory tier between DRAM and Flash that has a lower cost per GB than DRAM and a higher performance than Flash. Far memory works by disaggregating memory and allowing nodes or machines to access the memory of a remote node/machine via compute express link. Memory is the most contested and least elastic resource in a data center. Currently, servers can only use local memory, which may be scarce on the local system but abundant on other underutilized servers. With far memory, local machines can use remote machine’s memory. By introducing far memory into the memory tier and moving less frequently accessed data to far memory, the system can perform efficiently with low DRAM and reduce the total cost of ownership. Far memory uses a remote machine’s memory as a swap device, either by using idle machines or by building memory appliances that only serve to provide a pool of memory shared by many servers. This approach optimizes memory usage and reduces over-provisioning. However, far memory also has its own challenges. Swapping out memory pages to remote machines increases the failure domain of each machine, which can lead to a catastrophic failure of the entire cluster.

Four reasons your agency's security infrastructure isn't agile enough

There are four key considerations for integrating security architecture effectively in an agile environment - Cross-Functional Collaboration: Security experts must actively engage with developers, testers, and product owners. Collaborating with experts helps create a shared understanding of security requirements and facilitates quick resolution of security-related issues. Embedding security professionals within Agile teams can enhance real-time collaboration and ensure consistent security controls. Security Training and Awareness: Given the rapid pace of an Agile sprint, all team members should be equipped with the knowledge to write secure code. ... Foster a Security Culture: Foster a culture where security is seen as everyone's responsibility, not just the security team's. Adapt the organizational mindset to value security equally with other business objectives. ... Security Champions within Agile Teams: Identify and nurture 'Security Champions' within each Agile team. These individuals with a keen interest in security act as a bridge between the security team and their respective agile teams. They help promote security best practices, ensuring security is not overlooked amidst other technical considerations.

AI Legislation: Enterprise Architecture Guide to Compliance

Artificial intelligence (AI) tools are so easy to leverage that they can be used by anyone within your organization without technical support. This means that you need to keep a careful eye on, not just the authorized applications you leverage, but what AI tools your colleagues could be using without authorization. In leveraging AI tools to generate content for your organization, your employees could unwittingly input private data into the public instance of ChatGPT. Not only does this share that data with ChatGPT's vendor, OpenAI, but it actually trains ChatGPT on that content, meaning the AI tool could potentially output that information to another user outside of your organization. Alternatively, overuse of generative AI tools without proper supervision could lead to factual or textual errors being published to your customers. Gen AI tools need careful supervision to ensure they don't "hallucinate" or produce mistakes, as they are unable to self-edit. It's equally important to be able to report back to legislators on what AI is being used across your company, so they can see you're compliant. This will likely become a regulatory requirement in the near future.

Choosing a disaster recovery site

The first option is to set up your own secondary DR data center in a different location from your primary site. Many large enterprises go this route; they build out DR infrastructure that mirrors what they have in production so that, at least in theory, it can take over instantly. The appeal here lies in control. Since you own and operate the hardware, you dictate compatibility, capacity, security controls and every other aspect. You’re not relying on any third party. The downside of course, lies in cost. All of that redundant infrastructure sitting idle doesn’t come cheap. ... The second approach is to engage an external DR service provider to furnish and manage a recovery site on your behalf. Companies like SunGard built their business around this model. The appeal lies in offloading responsibility. Rather than build out your own infrastructure, you essentially reserve DR data center capacity with the provider. ... The third option for housing your DR infrastructure is leveraging the public cloud. Market leaders like AWS and Azure offer seemingly limitless capacity that can scale to meet even huge demands when disaster strikes.

How CISOs navigate policies and access across enterprises

Simply speaking, if existing network controls are now being moved to the cloud, the scope of technical controls does not drastically differ from legacy approaches. The technology, however, has massively evolved towards platform-centric controls, and that for a good reason. Isolated controls cause complexity, and if you are moving your perimeter to a hyperscaler, both your users and their devices will no longer be managed by the corporate on-prem security controls either. A good CASB to broker between user and data is key, as is identity and access management. What’s now new is workload protection requirements á la CSAP technology. In addition to increasing sophistication and the number of security threats and successful breaches, most enterprises further increase risk by “rouge IT” teams leveraging cloud environments without the awareness and management by security teams. Cloud deployments are typically deployed faster and with less planning and oversight than data center or on-site environment deployments. Cloud security tools should be an extension of your other premise-based tools for ease of management, consistency of policy enforcement and cost savings due to additional purchase commitments, training, and certification non-duplicity.

What to Know About Machine Customers

In the realm of customer service and support, machine customers are like virtual assistants or smart devices (think of Siri or Alexa) that carry out customer service tasks on behalf of actual human customers. Alok Kulkarni, CEO and Co-founder of Cyara, says the emergence of machine customers introduces a new dynamic, requiring organizations to adapt their existing support strategies. “This might include developing specific interfaces and communication channels tailored for interactions with machine customers,” he explains in an email interview. Organizations must create additional self-service options specifically designed for machine customers. “Unlike traditional customer support approaches, catering to machine customers requires a nuanced understanding of their specific needs and operational dynamics,” Kulkarni explains. This means designing self-service interfaces that are not only user-friendly for machines but also align with the intricacies of autonomous negotiation and purchasing processes. These interfaces should empower machine customers to navigate through various stages of transactions autonomously, from product selection to payment processing, ensuring a streamlined and frictionless experience.

Google: Govs Drive Sharp Growth of Commercial Spyware Cos

Much of the concern has to do with the explosion in the availability of tools and services that allow governments and law enforcement to break into target devices with impunity, harvest information from them, and spy unchecked on victims. The vendors selling these tools — most of which are designed for mobile devices — have often openly pitched their wares as legitimate tools that aid in law enforcement and counter-terrorism efforts. But the reality is that repressive governments have routinely used spyware tools against journalists, activists, dissidents, and opposition party politicians, said Google. The company's report cites three instances of such misuse: one that targeted a human rights defender working with a Mexico-based rights organization; another against an exiled Russian journalist; and the third against the co-founder and director of a Salvadorian investigative news outlet. The researcher attributes much of the recent growth in the CSV market to strong demand from governments around the world to outsource their need for spyware tools rather than have an advanced persistent threat build them in-house.

How To Build Autonomous Agents – The End-Goal for Generative AI

From a technology perspective, there are five elements that go into autonomous agent designs: the agent itself, for processing; tools, for interaction; prompt recipes, for prompting and planning; memory and context, for training and storing data; and APIs / user interfaces, for interaction. The agent at the center of this infrastructure leverages one or more LLMs and the integrations with other services. You can build this integration framework yourself, or you can bring in one of the existing orchestration frameworks that have been created, such as LangChain or LlamaIndex. The framework should provide the low-level foundational model APIs that your service will support. It connects your agent to the resources that you will use as part of your overall agent, including everything from existing databases and external APIs, to other elements over time. It also has to take into account what use cases you intend to deliver with your agent, from chatbots to more complex autonomous tasks. Existing orchestration frameworks can take care of a lot of the heavy lifting involved in managing LLMs, which makes it much easier and faster to build applications or services that use GenAI.

How Platform and Site Reliability Engineering Are Evolving DevOps

Actually, failure should not just be OK but welcome. Most organizations are averse to failure, but it’s only through our failures in these spaces that we can learn and grow and figure out how to best position, leverage, and continue to imagine the roles of DevOps, platform engineers, and SRE. I’ve seen this play out in large companies that went all in on DevOps and then realized that they needed a team focused on breaking down any barriers that presented themselves to developers. At scale, DevOps - even with the tools provided by the internally focused platform engineering team - didn’t really cut it. These companies then integrated the SRE function, which filled DevOps’ reliability and scalability gaps. That worked until these companies realized that they were reinventing the wheel - dozens of times. Different engineering teams within the organization were doing things just differently enough - different setups, different processes, different expectations - that they needed separate setups to put out a service. The SREs were seeing all of this after the fact, which led them to circle back to the realization that different teams needed to be using the same development building blocks. Frustrating? Yes. The cost of increasing efficiency in the future? Absolutely.

Quote for the day:

“It’s better to look ahead and prepare, than to look back and regret.” -- Jackie Joyner-Kersee