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"Always remember, your focus determines your reality." -- George Lucas

Agentic AI and Solution Architects

are intelligent systems designed to operate with autonomy, agency, and authority—three foundational concepts that define their ability to act independently, pursue goals on behalf of users, and make impactful decisions within defined boundaries. These systems are often built using a , where multiple specialized or generalist agents collaborate, either in centralized or decentralized environments. ... As (IT) architects we drive change that creates business opportunities through technical innovation. One of the key activities of a Solution Architect is to design solutions by applying methods and techniques combined with technical and business expertise. The actual solution design process will follow a similar pattern to that of a creative technology design process. An architect will combine and group the different components together according to stakeholder group and will, over several sessions, develop concept views related to key architectural components, establishing different options. Deciding the “right” option will mean balancing the various criteria like functionality, value for money, compliance, quality, and sustainability. IT architecture design involves complex decision-making, planning, and problem-solving that require human expertise and experience. That is where most of the architect’s work is focused on – using knowledge and experience to research a particular subject, to apply design thinking and to solve problems to establish a solution.

Shadow AI risk: Navigating the growing threat of ungoverned AI adoption

Only half (52%) of global organizations claim to have comprehensive controls in place, with smaller companies lagging even further behind. This lack of robust governance and visibility leaves organizations vulnerable to , compliance failures, and . For many organizations, AI controls are lacking. ... As AI systems become more autonomous and capable of acting on behalf of users, the risks grow even more complex. The rise of agentic AI, which can make decisions and take independent action within systems, amplifies the impact of weak identity security controls. As these advanced AI systems are given more control over critical systems and data, the potential risk of security breaches and compliance failures grows exponentially. To keep pace, security teams must evolve their identity security strategies to include these emerging machine entities, treating them with the same rigor as human identities. ... To effectively mitigate the risks associated with and ungoverned AI adoption, organizations need to start with a solid foundation of governance and visibility. That means implementing clear acceptable use guidelines, access controls, activity logging and auditing, and identity governance for AI entities. By treating AI entities as identities that are subject to the same authentication, authorization, and monitoring as human users, organizations can safely harness the benefits of AI without compromising security.

Secure Product Development Framework: More Than Just Compliance

Security risk assessment is a key activity that starts early in development and continues throughout the product life cycle through on-market support and eventual product retirement. references AAMI SW96, “Standard for medical device security - Security risk management for device manufacturers,” as a recommended standard for a security risk assessment process. Security risk assessment considers both safety and business security risks ... Implementing a clear and consistent security risk assessment process within the SPDF can also save time (and money). Focus can be placed on those areas of the design with the highest security risk, instead of on design areas with little to no security risk. Decisions on whether patches need to be applied in the field are easier to make when based on security risk. Leveraging the same security risk process across products and business areas allows teams to focus on execution rather than designing a new process. Once a product is launched, an SPDF can assist with managing that product. Postmarket SPDF activities include vulnerability monitoring/disclosure, patch management, and incident response. A critical component of vulnerability monitoring is the maintenance and continuous use of a software bill of materials (SBOM). The SBOM provides a machine-readable inventory of all custom, commercial, open-source, and third-party software components within the device.

Vibe Coding Can Create Unseen Vulnerabilities

does accelerate app prototyping and makes software collaboration easier, but it also has several shortcomings. Security is a serious concern. Large language models (LLMs) are inherently vulnerable to security risks when used by those without sufficient security experience. Moreover, the risk is amplified by the fact that AI is so flexible that it’s impossible to give out simple, universal rules on how to make AI write secure code for you. LLMs may use outdated libraries, lack input validation, or fail to follow secure practices. AI code generators also lack an understanding of trust boundaries and system architectures. When using vibe coding, programmer oversight and review are necessary to prevent these issues from entering production code. Working with black-box code also makes it difficult to provide context about the app. For example, improper configurations may expose internal logic by sending sensitive code snippets to external APIs. This can be a real problem in highly regulated industries with strict rules about code handling. Vibe coding also tends to add technical debt, accumulating unreviewed or unexplained blocks of code. Over time, these code blocks proliferate, creating a glut and making code maintenance more difficult. Since less experienced developers tend to use vibe coding, they can overlook security issues. Consider the recent Tea Dating Advice hack. A hacker was able to access 72,000 images stored in a public

The state of cloud-native computing in 2025

“We’ve reached a level of maturity in the cloud-native ecosystem that people might think that things are now a bit boring. While AI is a natural extension of and cloud-native architectures, there are changes required in the architecture to support AI workloads compared to previous workloads. Platform engineering continues to have strong customer interest… and new AI enhancements allow for even greater productivity for developers and operators. ...” said Miniman ... “However, runaway complexity and cost threaten to derail mass enterprise success. The modern observability stack has become an exorbitant black hole, delivering insufficient value for its exorbitant cost and demands a fundamental rethink of data management. Simultaneously, the gamble failed, proving too complex and expensive. The imperative is clear and necessitates pulling workloads back from the brink with democratized data management to pull workloads back onto central platforms,” said Zilka. ... “The focus has shifted from how quickly I can deploy, to how I can get a handle on costs and how resilient my platform is to changes or outages like we saw recently with . Teams are recognising the overhead these technologies have introduced for developers and are centralising that work. We’re seeing more platform teams set best practices, use tooling to enforce them and move from “adoption mode” to “operational excellence,” said Rajabi.

Insurability now a core test for boardroom AI & climate strategy

Organisations face growing threats from data poisoning and cyber-attacks, prompting insurers to play a more decisive role in risk management. Levent Ergin, Chief Climate, Sustainability & AI Strategist at , highlighted the increasing scrutiny on what businesses can insure against. ... AI is now a fixture at board meetings due to its direct impact on company valuation. However, he observes a gap between current boardroom discussions and the transformative potential of AI. "AI is now a standing item in every board meeting because it directly shapes valuation. Investors see it as a signal of how forward-thinking a company really is. But many boards are still asking the wrong question: 'How can we use AI to automate or augment our existing processes?' when they should be asking 'What's possible?' It's not just about automating what already exists; it's about reimagining how things are done. ..." said Hanson. ... "Too many businesses still treat AI projects like any other investment, where the return has to be quantified against a specific outcome. In truth, they should be budgeting for failure. The best innovators plan for things not to work first time, just as pharmaceutical companies or tech giants do, because even a 98% failure rate can still produce world-changing results. The moment we stop fearing failure and start funding it, we'll see genuine AI innovation break through," said Hanson.

Are we in a cyber awareness crisis?

To improve cyber awareness, organizations need to move beyond box-ticking exercises and build engagement through relevance and creativity. This is the advice of Simon Backwell, a member of the Emerging Trends Working Group at professional association , and head of information security at software company . He advocates for interactive, rather than static training, where employees can explore why something was suspicious, as they learn by doing, rather than guessing the right answer and moving on. ... Not only does AI present new risks from its use within the business, but also from the way criminals are using it. “ frequently use gen AI chatbots, and , such as robocall scams, now use deepfakes,” notes Candrick. “AI puts social engineering on steroids, yet cybersecurity leaders are still using the same awareness measures that were already insufficient.” While regulatory pressure will play a role in improving AI-related cybersecurity, regulations will always struggle to keep pace, especially in the UK where the process takes time. For example, the is only now filtering through, much like did back in 2018, says Backwell. But with how fast AI is advancing – almost weekly – these rules risk becoming outdated as soon as they’re released. ... “As board alignment weakens, CISOs have to work harder to translate cyber risk into business impact, because boards now rank business valuation as their top post-incident concern,” says Cooke.

How to build a supercomputer

When it comes to ’s architecture, , head of HPC/AI EMEA research lab, at , describes the design as “the architecture that HPE, with its great heritage, builds for the top systems.” A single cabinet in an EX4000 system can hold up to 64 compute blades – high-density modular servers that share power, cooling, and network resources – within eight compute chassis, all of which are cooled by direct-attached liquid-cooled cold plates supported by a cooling distribution unit (CDU). “It's super integrated," he says. “The back part, which is the whole network infrastructure (), matches the front part, which contains the blades.” For Hunter, HLRS has selected hardware, but Haus explains that with Cray EX systems, customers can, more or less, select their processing unit of choice from whichever vendor they want, and the compute infrastructure can be slotted into the system without the need to total reconfiguration. “Should HLRS decide at some point to swap [Hunter’s] AMD plates for the next generation, or use another competitor’s, the rest of the system stays the same. They could have also decided not to use our network – keep the plates and put a different network in, if we have that in the form factor. [HPE Cray EX architecture] is really tightly matched, but at the same time, it’s flexible," he says. Hunter itself is intended as a transitional system to the , which is due to go online in 2027.

The AI Reskilling Imperative: Bridging India's talent and gender gap

Policies should shift from less general policies to specific interventions. Initiatives such as and need to be bolstered with AI-specific courses available online in the local language. The government can: Sponsor and encourage scholarships and mentorships for Women in AI. Develop financial reward systems for companies reaching gender diversity in their AI teams. Introduce AI literacy and ethics into the national education system, beginning at the secondary school level. ... As the main consumer of AI talent, the private sector should be at the forefront. The first one is the skills-first approach to hiring, but reskilling as an ongoing investment is not an option. Companies should: Devote a huge proportion of CSR budgets to simple AI and digital literacy efforts, especially among women in low-income and rural communities; Launch internal reskilling programs to shift existing workers out of positions at risk of automation (e.g., manual software testing, simple data entry) and into new roles, such as AI integrators or data annotators; Embrace explicit ethical standards for the application of AI, including a workforce transition and support strategy. ... The universities will be obliged to redesign courses that incorporate AI's technical wisdom and infuse them with morals, critical thinking, and subject knowledge. Collaboration between industry and academia is important to ensure courses are practical and incorporate real-world projects.

Enterprises to focus AI spend on cost savings & data control

"CIOs will move from experimenting with AI to orchestrating it, governing outcomes, agents, and data. AI leadership will evolve from pilots to performance. CIOs will be accountable for tangible business outcomes, defining clear frameworks that connect AI investments to enterprise KPIs and ROI. That means managing a new hybrid workforce of humans and digital agents, complete with job descriptions, correlated KPIs and measurement standards, and governance guardrails. Yet none of this will succeed without secure information management, ensuring that the data fueling and training these agents is accurate, compliant, and trustworthy. Simply put, good data results in good AI outcomes. As AI accelerates, traditional network and security operations will be reimagined for an always-on, agent-driven enterprise, where value is derived as much from data discipline as from innovation itself," said Bell. ... "A Major brand fallout will force AI accountability. In the next year, we'll likely see a major brand face real damage from AI misuse. It won't be a cyberattack in the traditional sense but something more subtle, like a plain text prompt injection that manipulates a model into acting against intent. These attacks can force hallucinations, expose proprietary or sensitive information, or break customer trust in seconds. Enterprises will need to verify AI behavior the same way they secure their networks, by checking every input and output. The companies that build AI systems with accountability and transparency at the core will be those that keep their reputations intact," said Berry.

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Daily Tech Digest - November 12, 2025