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How AI Goes Beyond Traditional Cyber Protection

How AI Goes Beyond Traditional Cyber Protection

AI adds new security levels for real-time analysis, early threat detection, remediation, and more to secure Edge and IoT infrastructures.
May 12, 2022


According to Gartner, over 75% of enterprise-generated data will be created and processed outside traditional data centers or the cloud by 2025, which is where edge computing comes in. Today, edge computing is powering the next wave of digital transformation, improving business agility and creating new customer experiences. The technology helps cope with the massive amounts of data generated by IoT by addressing the need for localized computing power, cost and latency optimization, and decentralizing networks or the cloud. Besides, it meets the key business priorities of reducing network latency to milliseconds and cost optimization.

In short, edge computing can be thought of as a suite of solutions that facilitate data processing at the source of data generation, and in the context of IoT, this means that data sources are sensors or embedded devices.

As with all rapidly evolving technologies, the evaluation, deployment, and operation of edge computing solutions comes with security risks.

Edge and IoT are still vulnerable.

Did you know that a data breach can cost from $3.86 million to $4.24 million for companies? That’s the highest average total cost in the IBM report’s history.

For one, by increasing the size of the footprint using edge computing, you are inadvertently extending the surface area for attacks. For example, unsecure endpoints can be misused in distributed denial-of-service attacks or as entry points to core networks.

Nonetheless, edge computing has enormous potential, and as such, organizations need to carefully consider the security implications of it all to ensure they’re working on a protected and safe environment.

Edge computing + AI: Cyber protection made in heaven

As Internet of Things (IoT) devices and other sensors continue to generate data by the millisecond, computer and network infrastructure need to adapt to this increase in the volume of data that needs to be analyzed and processed for intake.

From the perspective of a cybercriminal, this is good news as they have more room to lurk in and deploy attacks, with numerous vulnerabilities open for exploitation. Once an attacker penetrates the edge, it’s a matter of seconds before malware propagates across the computing network.

Threats and security breaches at the edge mean more than simply a disruption to the service. As data piles on and on, cybercriminals are lured to edge applications like moths to a flame. Most security initiatives are focused on endpoints or the network itself, making the edge an easy prey.

As a point of reference, 66% of IT teams consider edge computing as a threat to organizations as security concerns escalate over the claim that issues will arise in the search for comprehensive security initiatives across all edge devices.

While many cybersecurity specialists are still on the fence about protecting edge computing separately instead of treating it as a whole with endpoints and network, it’s time to give visibility to a grey area that requires greater understanding with clear action and reaction plans in case of a threat at the edge.

The good news is that there is no greater technology out there than Artificial Intelligence (AI) that can meet any threat at the edge as robustly as AI does. AI goes beyond traditional cyber protection, adding new layers of sophistication and a superior protection wall for edge devices.

IBM reports that costs were significantly lower for some of organizations with a more mature security posture, and higher for organizations that lagged in areas such as security AI and automation, zero trust and cloud security.

AI in edge computing enables the complex execution of tasks, and importantly enough, it also helps with real-time analysis and detection of threats, from authentication, authorization, user management, and user repository techniques, AI can analyze historical data with machine learning models, monitoring for unusual activity in real time.

AI cybersecurity goes a long way in intruder detection and prevention, as AI systems can accurately detect even the slightest anomaly or subtle deviation from normal behavior, be it from a sophisticated attacker or a trusted insider. Besides, it monitors the operational cycle and predicts device failure.

Why does AI go beyond traditional cybersecurity? For one, human cybersecurity specialists and traditional tools frequently overlook threats with small deviations. In turn, AI algorithms are primed to identify patterns or abnormal behavior at the earliest possible stages thanks to ML algorithms that monitor the flow of data and identify the slightest pattern deviations before they can inflict any sort of damage.

AI in edge computing cybersecurity can take many forms. If we segment it based on technology, we can find machine learning, natural language processing, and context awareness to collect and handle big data with the increased ability to perform virtually impossible calculations.

AI also adds superior speed to process information and data churning, addressing privacy requirements on the go and supporting a stronger operational security standing. In addition, AI in edge computing protection can effectively employ more security capabilities without disrupting the performance of edge computing devices.

By addressing security concerns at the root, you can ensure your network is up to date and compliant with the latest standards, allowing you to aggressively pursue a growth strategy in a highly secure and compliant environment.


Digital transformation has revolutionized the way and the speed at which applications operate, with more traffic being directed to the edge, and as such, cybersecurity specialists need to understand what’s happening at the edge to create better policies and identify weak points in their protective walls of defense.

As hybrid networks become the norm and as endpoints multiply at a seemingly alarming rate, cybersecurity teams need to make edge computing safe. From architecture to the full range of a distributed system, it’s important to ensure only the right people and the right things have access to the right applications at the right time.

Independently on the industry, the threats, risks and vulnerability may damage any business. The market needs strategic approaches to cybersecurity enabled by cutting-edge, AI-powered technology.

By infusing AI in your cybersecurity endeavors in edge computing is a game changer in terms of data privacy, endpoint security, and productivity concerns. The automation of edge computing security holds potential to counteract many of the current challenges we face today as attackers are constantly looking for new entry points to puncture a hole in.

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