February 25, 2025
Kannan Subbiah
FCA | CISA | CGEIT | CCISO | GRC Consulting | Independent Director | Enterprise & Solution Architecture | Former Sr. VP & CTO of MF Utilities | BU Soft Tech | itTrident
Service as software, also referred to as SaaS 2.0, goes beyond layering AI atop existing applications. It centers on the concept of automating business processes through intelligent APIs and autonomous services. The framework aims to eliminate human input and involvement through AI agents that act and react to conditions based on events, behavioral changes, and feedback. The result is autonomous software. “Traditional SaaS provides cloud-based tools where staff still do the work. Service as software flips that script. Instead of having staff do the work, you're making calls to an API or using software that does the work for you,” says Mark Strefford, founder of TimelapseAI, a UK-based consulting firm. ... CIOs and IT leaders should start small and iterate, experts say. As an organization gains confidence and trust, it can expand the autonomy of a SaaS 2.0 component. “More AI initiatives have failed from starting too big than too small,” Strefford notes. Consequently, it’s critical to understand the entire workflow, build in oversight and protections, establish measurement and validation tools, and stay focused on outcomes. A few factors can make or break an initiative, Giron says. Data quality and the ability to integrate across systems is crucial. A framework for standardization is critical. This includes cleaning, standardizing, and preparing legacy data.?
The Well-Architected Framework provides a structured approach to making architectural decisions. While it originally focused on operational, security, and financial trade-offs, the Sustainability Pillar introduces specific guidance for designing cloud solutions with minimal environmental impact. One key architectural trade-off is between performance efficiency and sustainability. While performance efficiency emphasizes speed and low latency, these benefits often come at the cost of over-provisioning resources. A more sustainable approach involves optimizing compute resources to ensure they are only consumed when necessary. Serverless computing solutions, such as AWS Lambda or Azure Functions, help minimize idle capacity by executing workloads only when triggered. Similarly, auto-scaling for containerized applications, such as Kubernetes Horizontal Pod Autoscaler (HPA) or AWS Fargate, ensures that resources are dynamically adjusted based on demand, preventing unnecessary energy consumption. Another critical balance is between cost optimization and sustainability. Traditional cost optimization strategies focus on reducing expenses, but without considering sustainability, businesses might make short-term cost-saving decisions that lead to long-term environmental inefficiencies. For example, many organizations store large volumes of data without assessing its relevance, leading to excessive storage-related energy use.
Many now believe that the power and speed of quantum computing will enable us to address some of the biggest and most difficult problems our civilization faces. Problem-solving will be made possible by quantum computing’s unprecedented processing speed and predictive analytics. That is a remarkable near-term potential. Mckinsey & Company forecasts that Quantum Technologies could create an economic value in the market of up to $2 trillion by 2035. Quantum measuring and sensing is one field where quantum technologies have already made their appearance. Navigational devices and magnetic resonance imaging already employ it. Quantum sensors detect and quantify minute changes in time, gravity, temperature, pressure, rotation, acceleration, frequency, and magnetic and electric fields using the smallest amounts of matter and energy. Quantum will have a direct impact on many scientific fields, including biology, chemistry, physics, and mathematics. Industry applications will have an impact on a wide range of fields, including healthcare, banking, communications, commerce, cybersecurity, energy, and space exploration. In other words, any sector in which data is a component. More specifically, quantum technology has incredible potential to transform a wide range of fields, including materials science, lasers, biotechnology, communications, genetic sequencing, and real-time data analytics.
"There's a higher propensity for manufacturing organizations to have cloud connectivity just as a way of doing business, because of the benefits of the public cloud for manufacturing, like for predictive analytics, just-in-time inventory management, and things along those lines," he says, pointing to Transportation Security Administration rules governing pipelines and logistics networks as one reason for the difference. "There is purposeful regulation to separate the IT-OT boundary — you tend to see multiple kinds of ring-fence layers of controls. ... There's a more conservative approach to outside-the-plant connectivity within logistics and transportation and natural resources," Geyer says. ... When it comes to cyber-defense, companies with operational technology should focus on protecting their most important functions, and that can vary by organization. One food-and-beverage company, for example, focuses on the most important production zones in the company, testing for weak and default passwords, checking for the existence of clear-text communications, and scanning for hard-coded credentials, says Claroty's Geyer. "The most important zone in each of their plants is milk receiving — if milk receiving fails, everything else is critical path and nothing can work throughout the plant," he says.?
“When you talk about BIA and RTOs [recovery time objective], you shouldn’t be just checking boxes,” Ennamli says. “You’re creating a map that shows you, and your decision-makers, exactly where to focus efforts when things go wrong. Basically, the nervous system of your business.” ... “And when the rubber hits the road during an actual incident, precious time is wasted on less important assets while critical business functions remain offline and not bringing in revenue,” he says. ... It’s vital to have robust communication protocols, says Jason Wingate, CEO at Emerald Ocean, a provider of brand development services. “You’re going to want a clear chain of command and communication,” he says. “Without established protocols, you’re about as effective as trying to coordinate a fire response with smoke signals.” The severity of the incident should inform the communications strategy, says David Taylor, a managing director at global consulting firm Protiviti. While cybersecurity team members actively responding to an incident will be in close contact and collaborating during an event, he says, others are likely not as plugged in or consistently informed. “Based on the assigned severity, stemming from the initial triage or a change to the level of severity based on new information during the response, governance should dictate the type, audience, and cadence of communications,” Taylor says.
Traditional software testing faces challenges as organizations must assess codes to ensure they do not downgrade system performance or introduce bugs. Applications with extensive functionalities are time-consuming as they demand several test cases. They must ensure appropriate management, detailing their needs and advancing critical results in every scope. Nonetheless, smoke and regression testing ensures the same test cases are conducted, leading to time-consuming activities. The difficulty makes it hard for the traditional approach to have critical coverage of what is needed, and it is challenging to ensure that every approach can be tackled appropriately, channeling value toward the demanded selection. ... Using ML-driven test automation leads to increased efficiency in managing repetitive tasks. These automated measures ensure an accelerated testing approach, allowing teams to work with better activities. ML also integrates quality assessment into the software, marking an increasingly beneficial way to attend to individual requirements to ensure every software is assessed for high risk, potential failures and critical functions, which achieve a better post-deployment result. Additionally, using ML automation leads to cost savings, enabling testing cycles to have minimal operational costs as they are automated and prevent defects from being deployed within the software.?