Daily Tech Digest - August 22, 2025

---

Quote for the day:

“Become the kind of leader that people would follow voluntarily, even if you had no title or position.” -- Brian Tracy

---

Leveraging DevOps to accelerate the delivery of intelligent and autonomous care solutions

Fast iteration and continuous delivery have become standard in industries like e-commerce and finance. Healthcare operates under different rules. Here, the consequences of technical missteps can directly affect care outcomes or compromise sensitive patient information. Even a small configuration error can delay a diagnosis or impact patient safety. That reality shifts how DevOps is applied. The focus is on building systems that behave consistently, meet compliance standards automatically, and support reliable care delivery at every step. ... In many healthcare environments, developers are held back by slow setup processes and multi-step approvals that make it harder to contribute code efficiently or with confidence. This often leads to slower cycles and fragmented focus. Modern DevOps platforms help by introducing prebuilt, compliant workflow templates, secure self-service provisioning for environments, and real-time, AI-supported code review tools. In one case, development teams streamlined dozens of custom scripts into a reusable pipeline that provisioned compliant environments automatically. The result was a noticeable reduction in setup time and greater consistency across projects. Building on this foundation, DevOps also play a vital role in development and deployment of the Machine Learning Models. 

Tackling the DevSecOps Gap in Software Understanding

The big idea in DevSecOps has always been this: shift security left, embed it early and often, and make it everyone’s responsibility. This makes DevSecOps the perfect context for addressing the software understanding gap. Why? Because the best time to capture visibility into your software’s inner workings isn’t after it’s shipped—it’s while it’s being built. ... Software Bill of Materials (SBOMs) are getting a lot of attention—and rightly so. They provide a machine-readable inventory of every component in a piece of software, down to the library level. SBOMs are a baseline requirement for software visibility, but they’re not the whole story. What we need is end-to-end traceability—from code to artifact to runtime. That includes:Component provenance: Where did this library come from, and who maintains it? Build pipelines: What tools and environments were used to compile the software? Deployment metadata: When and where was this version deployed, and under what conditions? ... Too often, the conversation around software security gets stuck on source code access. But as anyone in DevSecOps knows, access to source code alone doesn’t solve the visibility problem. You need insight into artifacts, pipelines, environment variables, configurations, and more. We’re talking about a whole-of-lifecycle approach—not a repo review.

Navigating the Legal Landscape of Generative AI: Risks for Tech Entrepreneurs

The legal framework governing generative AI is still evolving. As the technology continues to advance, the legal requirements will also change. Although the law is still playing catch-up with the technology, several jurisdictions have already implemented regulations specifically targeting AI, and others are considering similar laws. Businesses should stay informed about emerging regulations and adapt their practices accordingly. ... Several jurisdictions have already enacted laws that specifically govern the development and use of AI, and others are considering such legislation. These laws impose additional obligations on developers and users of generative AI, including with respect to permitted uses, transparency, impact assessments and prohibiting discrimination. ... In addition to AI-specific laws, traditional data privacy and security laws – including the EU General Data Protection Regulation (GDPR) and U.S. federal and state privacy laws – still govern the use of personal data in connection with generative AI. For example, under GDPR the use of personal data requires a lawful basis, such as consent or legitimate interest. In addition, many other data protection laws require companies to disclose how they use and disclose personal data, secure the data, conduct data protection impact assessments and facilitate individual rights, including the right to have certain data erased. 

Five ways OSINT helps financial institutions to fight money laundering

By drawing from public data sources available online, such as corporate registries and property ownership records, OSINT tools can provide investigators with a map of intricate corporate and criminal networks, helping them unmask UBOs. This means investigators can work more efficiently to uncover connections between people and companies that they otherwise might not have spotted. ... External intelligence can help analysts to monitor developments, so that newer forms of money laundering create fewer compliance headaches for firms. Some of the latest trends include money muling, where criminals harness channels like social media to recruit individuals to launder money through their bank accounts, and trade-based laundering, which allows bad actors to move funds across borders by exploiting international complexity. OSINT helps identify these emerging patterns, enabling earlier intervention and minimizing enforcement risks. ... When it comes to completing suspicious activity reports (SARs), many financial institutions rely on internal data, spending millions on transaction monitoring, for instance. While these investments are unquestionably necessary, external intelligence like OSINT is often neglected – despite it often being key to identifying bad actors and gaining a full picture of financial crime risk. 

The hard problem in data centres isn’t cooling or power – it’s people

Traditional infrastructure jobs no longer have the allure they once did, with Silicon Valley and startups capturing the imagination of young talent. Let’s be honest – it just isn’t seen as ‘sexy’ anymore. But while people dream about coding the next app, they forget someone has to build and maintain the physical networks that power everything. And that ‘someone’ is disappearing fast. Another factor is that the data centre sector hasn’t done a great job of telling its story. We’re seen as opaque, technical and behind closed doors. Most students don’t even know what a data centre is, and until something breaks  it doesn’t even register. That’s got to change. We need to reframe the narrative. Working in data centres isn’t about grey boxes and cabling. It’s about solving real-world problems that affect billions of people around the world, every single second of every day. ... Fixing the skills gap isn’t just about hiring more people. It’s about keeping the knowledge we already have in the industry and finding ways to pass it on. Right now, we’re on the verge of losing decades of expertise. Many of the engineers, designers and project leads who built today’s data centre infrastructure are approaching retirement. While projects operate at a huge scale and could appear exciting to new engineers, we also have inherent challenges that come with relatively new sectors. 

Multi-party computation is trending for digital ID privacy: Partisia explains why

The main idea is achieving fully decentralized data, even biometric information, giving individuals even more privacy. “We take their identity structure and we actually run the matching of the identity inside MPC,” he says. This means that neither Partisia nor the company that runs the structure has the full biometric information. They can match it without ever decrypting it, Bundgaard explains. Partisia says it’s getting close to this goal in its Japan experiment. The company has also been working on a similar goal of linking digital credentials to biometrics with U.S.-based Trust Stamp. But it is also developing other identity-related uses, such as proving age or other information. ... Multiparty computation protocols are closing that gap: Since all data is encrypted, no one learns anything they did not already know. Beyond protecting data, another advantage is that it still allows data analysts to run computations on encrypted data, according to Partisia. There may be another important role for this cryptographic technique when it comes to privacy. Blockchain and multiparty computation could potentially help lessen friction between European privacy standards, such as eIDAS and GDPR, and those of other countries. “I have one standard in Japan and I travel to Europe and there is a different standard,” says Bundgaard. 

MIT report misunderstood: Shadow AI economy booms while headlines cry failure

While headlines trumpet that “95% of generative AI pilots at companies are failing,” the report actually reveals something far more remarkable: the fastest and most successful enterprise technology adoption in corporate history is happening right under executives’ noses. ... The MIT researchers discovered what they call a “shadow AI economy” where workers use personal ChatGPT accounts, Claude subscriptions and other consumer tools to handle significant portions of their jobs. These employees aren’t just experimenting — they’re using AI “multiples times a day every day of their weekly workload,” the study found. ... Far from showing AI failure, the shadow economy reveals massive productivity gains that don’t appear in corporate metrics. Workers have solved integration challenges that stymie official initiatives, proving AI works when implemented correctly. “This shadow economy demonstrates that individuals can successfully cross the GenAI Divide when given access to flexible, responsive tools,” the report explains. Some companies have started paying attention: “Forward-thinking organizations are beginning to bridge this gap by learning from shadow usage and analyzing which personal tools deliver value before procuring enterprise alternatives.” The productivity gains are real and measurable, just hidden from traditional corporate accounting. 

The Price of Intelligence

Indirect prompt injection represents another significant vulnerability in LLMs. This phenomenon occurs when an LLM follows instructions embedded within the data rather than the user’s input. The implications of this vulnerability are far-reaching, potentially compromising data security, privacy, and the integrity of LLM-powered systems. At its core, indirect prompt injection exploits the LLM’s inability to consistently differentiate between content it should process passively (that is, data) and instructions it should follow. While LLMs have some inherent understanding of content boundaries based on their training, they are far from perfect. ... Jailbreaks represent another significant vulnerability in LLMs. This technique involves crafting user-controlled prompts that manipulate an LLM into violating its established guidelines, ethical constraints, or trained alignments. The implications of successful jailbreaks can potentially undermine the safety, reliability, and ethical use of AI systems. Intuitively, jailbreaks aim to narrow the gap between what the model is constrained to generate, because of factors such as alignment, and the full breadth of what it is technically able to produce. At their core, jailbreaks exploit the flexibility and contextual understanding capabilities of LLMs. While these models are typically designed with safeguards and ethical guidelines, their ability to adapt to various contexts and instructions can be turned against them.

The Strategic Transformation: When Bottom-Up Meets Top-Down Innovation

The most innovative organizations aren’t always purely top-down or bottom-up—they carefully orchestrate combinations of both. Strategic leadership provides direction and resources, while grassroots innovation offers practical insights and the capability to adapt rapidly. Chynoweth noted how strategic portfolio management helps companies “keep their investments in tech aligned to make sure they’re making the right investments.” The key is creating systems that can channel bottom-up innovations while ensuring they support the organization’s strategic objectives. Organizations that succeed in managing both top-down and bottom-up innovation typically have several characteristics. They establish clear strategic priorities from leadership while creating space for experimentation and adaptation. They implement systems for capturing and evaluating innovations regardless of their origin. And they create mechanisms for scaling successful pilots while maintaining strategic alignment. The future belongs to enterprises that can master this balance. Pure top-down enterprises will likely continue to struggle with implementation realities and changing market conditions. In contrast, pure bottom-up organizations would continue to lack the scale and coordination needed for significant impact.

Digital-first doesn’t mean disconnected for this CEO and founder

“Digital-first doesn’t mean disconnected – it means being intentional,” she said. For leaders it creates a culture where the people involved feel supported, wherever they’re working, she thinks. She adds that while many organisations found themselves in a situation where the pandemic forced them to establish a remote-first system, very few actually fully invested in making it work well. “High performance and innovation don’t happen in isolation,” said Feeney. “They happen when people feel connected, supported and inspired.” Sentiments which she explained are no longer nice to have, but are becoming a part of modern organisational infrastructure. One in which people are empowered to do their best work on their own terms. ... “One of the biggest challenges I have faced as a founder was learning to slow down, especially when eager to introduce innovation. Early on, I was keen to implement automation and technology, but I quickly realised that without reliable data and processes, these tools could not reach their full potential.” What she learned was, to do things correctly, you have to stop, review your foundations and processes and when you encounter an obstacle, deal with it, because though the stopping and starting might initially be frustrating, you can’t overestimate the importance of clean data, the right systems and personnel alignment with new tech.

Daily Tech Digest - May 08, 2025

---

Quote for the day:

Don't fear failure. Fear being in the exact same place next year as you are today. - Unknown

---

Security Tools Alone Don't Protect You — Control Effectiveness Does

Buying more tools has long been considered the key to cybersecurity performance. Yet the facts tell a different story. According to the Gartner report, "misconfiguration of technical security controls is a leading cause for the continued success of attacks." Many organizations have impressive inventories of firewalls, endpoint solutions, identity tools, SIEMs, and other controls. Yet breaches continue because these tools are often misconfigured, poorly integrated, or disconnected from actual business risks. ... Moving toward true control effectiveness takes more than just a few technical tweaks. It requires a real shift - in mindset, in day-to-day practice, and in how teams across the organization work together. Success depends on stronger partnerships between security teams, asset owners, IT operations, and business leaders. Asset owners, in particular, bring critical knowledge to the table - how their systems are built, where the sensitive data lives, and which processes are too important to fail. Supporting this collaboration also means rethinking how we train teams. ... Making security controls truly effective demands a broader shift in how organizations think and work. Security optimization must be embedded into how systems are designed, operated, and maintained - not treated as a separate function.

APIs: From Tools to Business Growth Engines

Apart from earning revenue, APIs also offer other benefits, including providing value to customers, partners and internal stakeholders through seamless integration and improving response time. By integrating third-party services seamlessly, APIs allow businesses to offer feature-rich, convenient and highly personalized experiences. This helps improve the "stickiness" of the customer and reduces churn. ... As businesses adopt cloud solutions, develop mobile applications and transition to microservice architectures, APIs have become a critical foundation of technological innovation. But their widespread use presents significant security risks. Poorly secured APIs can be prone to becoming cyberattack entry points, potentially exposing sensitive data, granting unauthorized access or even leading to extensive network compromises. ... Managing the API life cycle using specialized tools and frameworks is also essential. This ensures a structured approach in the seven stages of API life cycle: design, development, testing, deployment, API performance monitoring, maintenance and retirement. This approach maximizes their value while minimizing risks. "APIs should be scalable and versioned to prevent breaking changes, with clear documentation for adoption. Performance should be optimized through rate limiting, caching and load balancing ..." Musser said.

How to Slash Cloud Waste Without Annoying Developers

Waste in cloud spending is not necessarily due to negligence or a lack of resources; it’s often due to poor visibility and understanding of how to optimize costs and resource allocations. Ironically, Kubernetes and GitOps were designed to enable DevOps practices by providing building blocks to facilitate collaboration between operations teams and developers ... ScaleOps’ platform serves as an example of an option that abstracts and automates the process. It’s positioned not as a platform for analysis and visibility but for resource automation. ScaleOps automates decision-making by eliminating the need for manual analysis and intervention, helping resource management become a continuous optimization of the infrastructure map. Scaling decisions, such as determining how to vertically scale, horizontally scale, and schedule pods onto the cluster to maximize performance and cost savings, are then made in real time. This capability forms the core of the ScaleOps platform. Savings and scaling efficiency are achieved through real-time usage data and predictive algorithms that determine the correct amount of resources needed at the pod level at the right time. The platform is “fully context-aware,” automatically identifying whether a workload involves a MySQL database, a stateless HTTP server, or a critical Kafka broker, and incorporating this information into scaling decisions, Baron said.

How to Prevent Your Security Tools from Turning into Exploits

Attackers don't need complex strategies when some security tools provide unrestricted access due to sloppy setups. Without proper input validation, APIs are at risk of being exploited, turning a vital defense mechanism into an attack vector. Bad actors can manipulate such APIs to execute malicious commands, seizing control over the tool and potentially spreading their reach across your infrastructure. Endpoint detection tools that log sensitive credentials in plain text worsen the problem by exposing pathways for privilege escalation and further compromise. ... If monitoring tools and critical production servers share the same network segment, a single compromised tool can give attackers free rein to move laterally and access sensitive systems. Isolating security tools into dedicated network zones is a best practice to prevent this, as proper segmentation reduces the scope of a breach and limits the attacker's ability to move laterally. Sandboxing adds another layer of security, too. ... Collaboration is key for zero trust to succeed. Security cannot be siloed within IT; developers, operations, and security teams must work together from the start. Automated security checks within CI/CD pipelines can catch vulnerabilities before deployment, such as when verbose logging is accidentally enabled on a production server. 

Fortifying Your Defenses: Ransomware Protection Strategies in the Age of Black Basta

What sets Black Basta apart is its disciplined methodology. Initial access is typically gained through phishing campaigns, vulnerable public-facing applications, compromised credentials or malicious software packages. Once inside, the group moves laterally through the network, escalates privileges, exfiltrates data and deploys ransomware at the most damaging points. Bottom line: Groups like Black Basta aren’t using zero-day exploits. They’re taking advantage of known gaps defenders too often leave open. ... Start with multi-factor authentication across remote access points and cloud applications. Audit user privileges regularly and apply the principle of least privilege. Consider passwordless authentication to eliminate commonly abused credentials. ... Unpatched internet-facing systems are among the most frequent entry points. Prioritize known exploited vulnerabilities, automate updates when possible and scan frequently. ... Secure VPNs with MFA. Where feasible, move to stronger architectures like virtual desktop infrastructure or zero trust network access, which assumes compromise is always a possibility. ... Phishing is still a top tactic. Go beyond spam filters. Use behavioral analysis tools and conduct regular training to help users spot suspicious emails. External email banners can provide a simple warning signal.

AI Emotional Dependency and the Quiet Erosion of Democratic Life

Byung-Chul Han’s The Expulsion of the Other is particularly instructive here. He argues that neoliberal societies are increasingly allergic to otherness: what is strange, challenging, or unfamiliar. Emotionally responsive AI companions embody this tendency. They reflect a sanitized version of the self, avoiding friction and reinforcing existing preferences. The user is never contradicted, never confronted. Over time, this may diminish one’s capacity for engaging with real difference; precisely the kind of engagement required for democracy to flourish. In addition, Han’s Psychopolitics offers a crucial lens through which to understand this transformation. He argues that power in the digital age no longer represses individuals but instead exploits their freedom, leading people to voluntarily submit to control through mechanisms of self-optimization, emotional exposure, and constant engagement. ... As behavioral psychologist BJ Fogg has shown, digital systems are designed to shape behavior. When these persuasive technologies take the form of emotionally intelligent agents, they begin to shape how we feel, what we believe, and whom we turn to for support. The result is a reconfiguration of subjectivity: users become emotionally aligned with machines, while withdrawing from the messy, imperfect human community.

From prompts to production: AI will soon write most code, reshape developer roles

While that timeline might sound bold, it points to a real shift in how software is built, with trends like vibe coding already taking off. Diego Lo Giudice, a vice president analyst at Forrester Research, said even senior developers are starting to leverage vibe as an additional tool. But he believes vibe coding and other AI-assisted development methods are currently aimed at “low hanging fruit” that frees up devs and engineers for more important and creative tasks. ... Augmented coding tools can help brainstorm, prototype, build full features, and check code for errors or security holes using natural language processing — whether through real-time suggestions, interactive code editing, or full-stack guidance. The tools streamline coding, making them ideal for solo developers, fast prototyping, or collaborative workflows, according to Gartner. GenAI tools include prompt-to-application tools such as StackBlitz Bolt.new, Github Spark, and Lovable, as well as AI-augmented testing tools such as BlinqIO, Diffblue, IDERA, QualityKiosk Technologies and Qyrus. ... Developers find genAI tools most useful for tasks like boilerplate generation, code understanding, testing, documentation, and refactoring. But they also create risks around code quality, IP, bias, and the effort needed to guide and verify outputs, Gartner said in a report last month.

Navigating the Warehouse Technology Matrix: Integration Strategies and Automation Flexibility in the IIoT Era

Warehouses have evolved from cost centers to strategic differentiators that directly impact customer satisfaction and competitive advantages. This transformation has been driven by e-commerce growth, heightened consumer expectations, labor challenges, and rapid technological advancement. For many organizations, the resulting technology ecosystem resembles a patchwork of systems struggling to communicate effectively, creating what analysts term “analysis paralysis” where leaders become overwhelmed by options. ... Among warehouse complexity dimensions, MHE automation plays a pivotal role—and it is easy to determine where you are on the Maturity Model. Organizations at Level 5 in automation automatically reach Level 5 overall complexity due to the integration, orchestration and investment needed to take advantage of MHE operational efficiencies. ... Providing unified control for diverse automation equipment, optimizing tasks and simplifying integration. Put simply, this is a software layer that coordinates multiple “agents” in real time, ensuring they work together without clashing. By dynamically assigning and reassigning tasks based on current workloads and priorities, these platforms reduce downtime, enhance productivity, and streamline communication between otherwise siloed systems.

How AI-Powered OSINT is Revolutionizing Threat Detection and Intelligence Gathering

Police and intelligence officers have traditionally relied on tips, informants, and classified sources. In contrast, OSINT draws from the vast “digital public square,” including social media networks, public records, and forums. For example, even casual social media posts can signal planned riots or extremist recruitment efforts. India’s diverse linguistic and cultural landscape also means that important signals may appear in dozens of regional languages and scripts – a scale that outstrips human monitoring. OSINT platforms address this by incorporating multilingual analysis, automatically translating and interpreting content from Hindi, Tamil, Telugu, and more. In practice, an AI-driven system can flag a Tamil-language tweet with extremist rhetoric just as easily as an English Facebook post. ... Artificial intelligence is what turns raw OSINT data into strategic intelligence. Machine learning and natural language processing (NLP) allow systems to filter noise, detect patterns and make predictions. For instance, sentiment analysis algorithms can gauge public mood or support for extremist ideologies in real time​. By tracking language trends and emotional tone across social media, AI can alert analysts to rising anger or unrest. In one recent case study, an AI-powered OSINT tool identified over 1,300 social media accounts spreading incendiary propaganda during Delhi protests. 

How to Determine Whether a Cloud Service Delivers Real Value

The cost of cloud services varies widely, but so does the functionality they offer. This means an expensive service may be well worth the price — if the capabilities it offers deliver a great deal of value. On the other hand, some cloud services simply cost a lot without providing much in the way of value. For IT organizations, then, a primary challenge in selecting cloud services is figuring out how much value they generate relative to their cost. This is rarely straightforward because what is valuable to one team might be of little use to another. ... No one can predict how cloud service providers may change their pricing or features in the future, of course. But you can make reasonable predictions. For instance, there's an argument to be made (and I will make it) that as generative AI cloud services mature and AI adoption rates increase, cloud service providers will raise fees for AI services. Currently, most generative AI services appear to be operating at a steep financial loss — which is unsurprising because all of the GPUs powering AI services don't just pay for themselves. If cloud providers want to make money on genAI, they'll probably need to raise their rates sooner or later, potentially reducing the value that businesses leverage from generative AI.