How Emerging Technologies Are Changing the Way Security Is Approached

Security used to follow a predictable path. Systems were built, then protected, often as a separate step that came later in the process. 

However, that approach doesn’t hold up anymore as new technologies are being deployed so quickly that security has to be part of the process from the start. A company rolling out a new app, for instance, can’t wait until launch day to think about vulnerabilities, especially when user data is already moving through multiple systems behind the scenes.

A closer look at today’s tech environment shows how interconnected everything has become. Security now involves understanding how all the moving parts interact, not just protecting one isolated platform.

Specialized Expertise Is Becoming Essential

To truly understand the new world of cyber security today requires more than a simple understanding of technical skills. There are new technologies. They come with new risks associated with them, but not all of these risks will always be obvious to someone looking at them on their own. 

Some of the most qualified people to help assess the risk involved will be those trained in cyber security who have an ability to step back and see how everything works together and  see where there will be vulnerabilities in the system, and to fix the problems before they become real issues. 

Many acquire this level of insight through an online MS in cybersecurity, which allows them to stay active in the field while expanding their expertise. This way, someone already working in IT support or network management can study real-world threat scenarios and apply those insights immediately to their current systems. 

Online programs tend to attract people who need that flexibility, especially those balancing work and learning at the same time. 

Artificial Intelligence Introduces New Risk Dimensions

Artificial intelligence systems bring a completely different set of concerns compared to traditional software. 

A large part of securing these technologies today involves securing the training of the models, securing the data going into them. And securing the system that produces the output as a result of the models being trained on the data. 

For example, the recommendation engine for a streaming media service is highly based on user data that it has collected from users. If this data was manipulated in some small way, the end result could be something totally different than what a user would expect based upon their experience with that service. 

Another example can be seen in automated hiring tools. If the data used to train the system carries bias, the results may reflect that bias, even if unintentionally. Protecting AI systems means looking at how data is collected, how it’s processed, and how outcomes are monitored over time. 

IoT Expands the Scope of Security

Connected devices have pushed security beyond traditional boundaries. A smart office setup might include connected thermostats, security cameras, badge entry systems, and even smart lighting. Each of these devices communicates with a network, creating multiple entry points that need attention.

For instance, if a manufacturing facility had a number of IoT sensors deployed to collect data regarding the overall performance of its machinery, should one of those sensors be compromised, it would provide a potential point of entry to the facility’s entire network or would result in a total disruption of the manufacturing process. 

To manage such an environment requires a high level of consistency across devices and devices must be managed properly in order to prevent someone from exploiting a single device/endpoint that would allow them entry to the rest of the system. 

Blockchain Reshapes Trust and Control

Blockchain introduces a different approach to handling data. Instead of relying on a single authority, information is distributed across multiple nodes. This can be seen in financial transactions using cryptocurrency, where validation happens across the network rather than through a central institution.

In the case of smart contracts, the relationship between the code and the transaction on the blockchain is a combination of a series of codes that when executed, are validated. The only way to secure them is through thorough review of the code by cyber security professionals.  

Who are able to develop an understanding of how these codes were put together and validated prior to creating the smart contract.  

APIs Drive Constant Data Exchange

APIs connect systems that were never originally designed to work together. A food delivery app, for instance, might connect to restaurant systems, payment gateways, and mapping services all at once. Each of these connections relies on data moving back and forth in real time.

Each point in a connection requires protection. When APIs are not configured securely, sensitive information could be revealed, and authorizations to access that information may be granted. A financial institution’s API that is not properly handled could provide access to account information as well as transaction histories. 

Securing connections through API management requires having controls for who can gain access, as well as the ability to monitor activity performed through the API. 

Digital Identity Requires Stronger Verification

With more and more services moving to the internet for delivery of services, identity has become the gatekeeper to accessing everything – from banking to healthcare. Logging in to a website is no longer about simply entering your username and password. If there is a weak verification process, it can open doors to account takeovers, especially when there is sensitive information being managed. 

Take online banking as an example. Many platforms now use multi-step verification, such as sending a code to a phone or requiring biometric confirmation. This is a response to how easily passwords alone can be compromised. Security in this area now focuses on layering verification methods.

Real-Time Systems Need Continuous Monitoring

Many modern systems are operating in real time – they process incoming data as it arrives and are incapable of halting operations for security checks. An example is stock trading systems, which automatically process transactions as they occur without any delay waiting for security checks. Therefore when a problem occurs, it must be corrected immediately. 

Instead of periodic reviews, systems rely on continuous observation. Alerts, automated responses, and live tracking become essential. Security becomes an active, ongoing process rather than something checked at intervals.

Machine Learning Helps Detect Unusual Activity

Machine learning tools can also be employed as a nondiscriminatory means of identifying behaviors that fall outside of anticipated parameters. An example of this would be a credit card company detecting a purchase made in a foreign country. 

The cardholder’s charge would create a flag on their account within minutes and the system would show that the cardholder made a second purchase in the same geographical region where they made a purchase from the foreign country to help prevent fraud from becoming much larger before being detected. 

At the same time, these systems need protection as well. If the data feeding into the model is manipulated, the detection system can be misled. Maintaining the integrity of these tools becomes just as important as using them, adding another layer to how security is handled.

Quantum Computing Challenges Current Encryption

There are many systems currently using encryption technology that is based upon an assumption of a certain level of computational complexity. Quantum computing possibilities represent a step change in computational capability for processing information, which means that the existing encryption technology may not protect the data transferred from system to system. 

For example, encrypted financial transactions or stored data could become vulnerable if current encryption methods are no longer effective. As such, this has led to the development of new cryptographic approaches designed to withstand future computing capabilities. Security planning now includes preparing for technologies that are still evolving, rather than reacting after they arrive.

As new technologies emerge, they continue to change the way security is being handled at all levels. The number of connected devices has increased; hence, there is a greater amount of data travelling at a faster rate, making the potential impact of any one vulnerability much more expansive across multiple layers. 

In order for any organization to remain prepared against these evolving threats, IT organizations must adopt a strategic approach as well as employ skilled personnel and stay current with changing strategy.

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