Engineering Trust is became an essential key in the future of enterprise security. Digital business requires a resilient and efficient IT foundation at its core. To scale cost-efficiently, a well-designed is a fundamental way. In general, IT is responsible for engineering the trust of a big percentage of enterprises. Adding technologies that enable the organization to scale its digitalization efforts is also an important consideration. Business fusion teams must also manage innovation and digitization, as IT alone cannot embrace this sculpting change.

From the general view; to catch the future of enterprise security trusted digital connections for enterprises people, and devices everywhere are needed. Solutions to fastly scale digital creativity anywhere will bring harmony to rapidity. Also, innovative abilities and capabilities would expedite business growth starting from today. When the foundation and building blocks are established, focusing on technology trends maximizes the value of what the organization creates. These technologies would exemplify the IT force multipliers to win business market share and accelerate growth.

Security in the Internet of Things

With its network of “smart,” sensor-enabled devices that can communicate and coordinate with one another via the Internet, the IoT could facilitate computer-mediated strategies for conducting business.

From the general view’s perspective, there are some factors to improve for enterprises when the topic is IoT. Enterprises should fill the gap in technical sophistication. Current Internet of Things security technology could be insufficiently sophisticated. Also, organizations should cope with the lack of effective end-to-end security solutions that use leading-edge components. On the other hand, immature security standards could be an issue. 

Large players would compete to establish proper standards. Industry organizations could be slow to create regulations that keep pace with technological advances. Also, uncertain knowledge among stakeholders about how standards and regulations would be set. Incidentally, the inability to understand the value of IoT security fully could be a back-stepper for enterprises. Customers and end-users of the organization’ could lack insight and knowledge about the IoT devices. From another view, monetizing problems in security solutions could occur.


Lack of awareness about the importancy of the enhanced security systems could lead customers to be unwilling to pay. So, this could stir up enterprises to have difficulties moving into brand-new software solutions. In short; with engineering trust, sculpting change, and accelerating growth enterprises would start to exemplify the future of their future of security. Let’s dive into details on all factors that could strengthen security.

Data Fabric

Determining current data utilization patterns and identifying priority areas for ongoing operations would be a great start to introducing data fabric solutions via metadata. Define and prioritize areas that have substantial deviation.

In this age, all of us know how valued data is. But it is hard to say the same thing for its usage related to the siloed applications. With the integration of data fabric via data across platforms and users, data could be available anywhere it is needed. Data fabric could be able to read which data is used via inbuilt analytics on metadata.

Cybersecurity Mesh

Interoperability and composability could be an enterprise’s first factors to consider while determining security solutions. Composing a common base framework to create and integrate brand-new security solutions. Distributed digital business assets around data centers and cloud architecture would become more preferable when it is compared with old-fashioned security approaches. Fragmented security focuses on enterprise perimeters would leave organizations open to breaches.

Having a composable approach to security is possible with a cybersecurity mesh architecture. Enterprises could enable a security structure for all assets with a common integrated structure along the foundation of IT services. And with this, enterprises that adopt a cybersecurity mesh architecture to work as a cooperative ecosystem will be able to decrease their financial impact of security incidents averagely of 90%.


Cloud-native Platforms / Multi-cloud Environments

The transition to cloud-native platforms and multi-cloud environments is also one of the important steps to keep up with the IoT future. First, it is necessary to minimize as much as possible remove-and-replace migrations, which basically do not take full advantage of cloud capabilities. Embrace modern application architectures and get rid of platforms trying to put legacy workloads in the cloud. These workloads, which require a lot of unnecessary maintenance, are not designed for the cloud, so they cannot take advantage of the desired benefits and thus create a load. Look for uses that provide rapid time to value that leverage the core flexibility and scalability of cloud computing. The goal is to reduce dependencies on infrastructure while gaining time to focus on application functionalities. Based on all these reasons; cloud-native platforms are predicted to serve as the foundation for more than 95% of new digital initiatives and ventures.

Composable Actions

Buy standard PBCs in-app marketplaces while advocating composable architecture principles in application modernization, selection of new engineering, and vendor services. Fusion teams may lack engineering coding skills as they will be selected from your business employees. Free them from being locked into the wrong technologies with their PBCs and software-enabled business objects with unified applications.

In this way, you can reach fast delivery. In short; Build reusable modules of software-defined business objects that fusion teams can self-assemble and reduce the time to market for these applications to be built quickly.

By 2024, the design mantra for new SaaS and custom applications will be “composable API-first or API-only,” rendering traditional SaaS and custom applications as “legacy.”

Decision intelligence

Make decision intelligence a part of your processes to scale and accelerate your business decisions with automation. The future; will be based on more data-driven support and AI-powered growth and improvements.

In line with decisions influenced by experiences and prejudices, enterprises should advance important decisions faster and more accurately when it comes to digital transformation. Improving corporate decision-making is possible with decision intelligence modeling through a framework. Thus, decisions based on learning and feedback become more effectively managed, evaluated, and improved by fusion teams. Building decision intelligence platforms; Along with integrating data analytics and artificial intelligence, it also brings automation by supporting and increasing decisions.

Decision intelligence improves corporate decision-making by modeling decisions through a framework. Fusion teams can manage, evaluate and improve decisions based on learning and feedback. Integrating data, analytics and AI enables decision intelligence platforms to be built to support, augment and automate decisions. Today; Product-centric organizations use decision intelligence to create competitive advantage, evaluate past decisions, and analyze competitor strategies. More than a third of large organizations are predicted to hire analysts who apply decision intelligence in the near future.


With holistic mapping and prioritization of collective initiatives, get rid of islands of task automation used to deliver synergetic and coordinated business results. Growth, digitization, and a shifting focus on operational excellence have driven the need for automation to expand. Hyperautomation, a business-oriented approach to identifying, controlling, and automating the IT process, is a good approach to managing as much work as possible. 

Hyperautomation, which requires the use of multiple technology tools and platforms, has begun to increase the rate of use of RPA, low-code platforms, and process mining tools. Decisions about what to automate are made strategically and based on targeted business outcomes for quality, time to market, business agility, or innovation for new business models. In short, as adaptive governance becomes a differentiating factor in corporate performance, the expense and effort of owning pervasive hyper-automation are projected to increase 40 times in a few years.

AI Engineering

Apply AI engineering as a strategic differential to create and sustain intelligence value. Build and refine AI engineering practices that include best practices from DataOps, ModelOps, and DevOps. To deliver game-changing solutions, organizations must embrace and optimize AI. It should be based on delivering consistent business value from AI by operationalizing updates to AI models using integrated data and model and development pipelines. This creates automated update pipelines combined with strong AI governance. From the future perspective, it is estimated that 10% of businesses that create AI will generate at least three times more value from their AI efforts than 90% of businesses that don’t.


Instruct teams following experience improvement initiatives to partner with and learn from others. Align all leaders of experience-related initiatives with their responsibilities to solve the combined needs of customers and employees. Total experience combines the disciplines of “customer experience, user experience, employee experience, and multi-experience to create a better experience for consumers and employees”. The aim is to connect each discipline and develop a more holistic and general experience for all stakeholders. 


Enterprises today using the total experience approach quickly learn customer behaviors with analytics and AI to proactively respond to the customer’s next action and create realistic training simulations for staff. Thanks to unified identity services; It helps the client move easily with self-service recruitment while providing integration with the consultant’s vision at multiple touchpoints. In the future, 60% of large businesses are expected to transform their business models with total experience to achieve world-class levels of customer satisfaction and employee advocacy.

Autonomic Systems

Adopting pilot autonomous technologies early provides agility and performance benefits in managing complex software and physical systems. As organizations grow, traditional manual management cannot scale at the same rate. Self-directed physical or software systems that learn from their environment are autonomous systems. They differ from automated systems in their ability to dynamically change their algorithms without software updates. Accordingly, they respond quickly to change while providing management at the scale of complex environments.

Distributed Enterprise

Due to the widespread use of remote working, there have been changes in customer and consumer behavior. Turn business models and gain market share by adopting “virtual first, remote-first” architectural principles. The distributed business, born from 2 domains, first met the need of remote workers for different tools and more flexibility. On the other hand, consumers are increasingly out of reach by traditional, physical means. In other words; By adopting a virtual and remote architectural approach, the distributed enterprise digitizes consumer touchpoints and creates experiences to support products. 

In the future, 75% of organizations that take advantage of distributed enterprise benefits are projected to achieve revenue growth 25% faster than their competitors. And also; We will be encountering trends such as digital dressing rooms that allow customers to try on styles virtually, the use of geolocation to match with consultants, and the use of drones, which is expected to increase a hundredfold.

Generative AI

Accelerate content production and R&D by choosing uses of AI. Thus, accelerating the creation of new products and increasing their personalization. No matter how much the AI ​​is trained to produce results, true force impact technologies can innovate on their own. Generative AI is a form of AI that learns a digital representation of sampled artifacts. It is used to create new, original realistic artifacts that retain similarity to training data but do not repeat. Thus, AI has begun to act as a rapid innovation engine for businesses. By 2025, productive AI is expected to account for 10% of all data generated, compared to less than 1% today.

To sum up; with engineering trust, sculpting change, and accelerating growth enterprises should catch the security trends of IoT. Are you looking for a partner to clutch the future? Cool Digital Solutionson your service!