To achieve a lower carbon operating model, the energy sector has recognized the importance of adopting advanced technologies such as artificial intelligence, industrial automation, and edge computing. These complex technologies enable effective management of operations and ensure a safe and reliable energy supply. However, their adoption also increases the cyber risk to the distributed edge environment, and thus cybersecurity measures are crucial.
According to the 2022 AT&T Cybersecurity Insights Report: Securing the Edge. Focus on Energy and Utilities, 79 percent of energy and utility respondents believe that there is a high chance of compromise in one of the use cases intended for production within the next three years. They reported that the security plan has to be robust with a look at all the security oversights and potential pitfalls that could impact the innovation enabled by edge computing.
Edge computing has been a key technology for companies undergoing digital transformation, which stakeholders perceive differently, depending on its value for the business. This makes it complex for companies to adopt security solutions and prevent cyber threats in the energy sector. Cyber security teams believe that secure access service edge (SASE) is better suited for distributed cloud-based networks and edge use cases.
But, the concept of SASE was primarily developed for cloud-based networks that would combine networking functionalities with security features, making it a heterogeneous solution. This security approach only works in online mode, which makes it impossible to detect an ongoing threat to the energy infrastructure with unstable and/or offline connections. At AI EdgeLabs, our security platform adopts cutting-edge technologies particularly developed for edge computing scenarios, serving the energy and utilities industry, and also operates effectively in online and offline modes.
In one of our previous articles, we discussed how the AI EdgeLabs security solution outperforms the SASE security model.
AT&T report suggests that remote control operations are one of the top use cases of edge computing technology in the energy sector. This technology has the ability to accelerate autonomous operations through software and enable industrial organizations to adopt remote staffing and centralized and flexible resourcing. It also helps to offload clouds and ensure real-time maintenance.
With Edges becoming proactive and a lot of data processing being done there, energy industry security experts consider intrusion and threat detection, network access restrictions, encrypted traffic, and firewall at edge computing as some of the most efficient security solutions. There is a complex and diverse landscape of security solutions with their own unique features and capabilities. While these solutions may effectively address specific vulnerabilities or endpoints, they often do not provide comprehensive coverage for the entire edge infrastructure.
Companies can adopt AI EdgeLabs, designed with a multi-layered security architecture that utilizes a holistic approach to edge security, implementing security controls such as smart firewalls, intrusion detection and prevention systems (IDS/IPS), and endpoint security solutions to protect the edge from cyber threats. Besides, our focus also includes edge network security to protect the network infrastructure that supports edge devices and applications and the data that flows through the network from unauthorized access, interception, or tampering.
The most serious concern in the energy sector is:
Sniffing attacks against the radio access network
A sniffing attack is when an attacker intercepts and reads network traffic. In the case of the energy industry, the hacker analyzes radio signals used to transmit data within the radio access network, which is used to connect remote IoT devices, such as sensors.
Organizations implement traditional security measures such as firewalls and intrusion detection systems. Along with supporting the conventional security features, AI EdgeLabs provides an advanced security solution with an autonomous cyber AI with smart dynamic firewalling, incorporating trusted hardware and IP filtering. This cutting-edge approach enables the quick interruption of ongoing cyberattacks within seconds.
The AI EdgeLabs platform also has an AI-based non-deterministic L2/L3 firewall, which uses machine learning models that can automatically block IP communication. Some rules are given to the trained machine learning models that can track and block malware cybersecurity threats.
Three other attacks have become a concern for the energy sector:
Attacks against the 5G core networks
Attacks against the endpoint devices
To keep the infrastructure, network, and edge devices secure, organizations need to plan their cybersecurity controls for edge use cases.
Another security measure essential for the energy sector is the zero-trust approach that helps to mitigate the effects of malware attacks. This security model requires authentication and verification before granting access to resources and applications.
To combat these major cyber attack concerns against the energy sector, there needs to have a security solution combining advanced security features like AI-based firewalls, autonomous cyber AI, behavior analysis, and a zero-trust approach. We at AI EdgeLabs, have understood this and designed the security platform so that if there is an attack on the system, we can mitigate it effectively in real-time. We are now able to detect more than 3000 different types of malware and ransomware attacks.
Particularly, the impact of DDoS attacks on the energy sector is identified as an area of least concern across all industries. Stakeholders believe that DDoS may not be as destructive as other attacks since they target the availability of the system rather than its confidentiality and integrity.
Despite this, stakeholders need to determine the potential impact of DDoS attacks and the operational efficiency of edge computing use cases while under a sustained DDoS attack. The cybersecurity team must evaluate the cyber risk and implement appropriate security measures to protect the system.
AI EdgeLabs simplifies the process of mitigating DDoS attacks by leveraging cyber AI and automation solutions. Our security platform uses real-time monitoring through machine learning algorithms that analyze network traffic and provides insights if any anomaly is detected. The automated response incorporated within our security platform enables organizations to defend their distributed edge infrastructure against DDoS attacks by blocking malicious network traffic.
The main features of AI EdgeLabs that secure the IoT edge infrastructure from cyber threats and attacks in the energy industry are threat detection and prevention (IDS & IPS), advanced network-based asset discovery for IoT, malware protection, endpoint detection and response (EDR), next-gen firewall, autonomous incident response and remediation.
The security solution is designed to be seamlessly deployed directly to the edge or IoT gateway across distributed infrastructures within an hour, which provides strong cybersecurity advantages to enterprises. The platform can detect and prevent cyberattacks in real-time, even in offline mode or unstable connectivity, while safeguarding sensitive information processed on the edge device or computer.
All of these edge devices are developed to support widely used communication protocols, such as Modbus, OPC UA, MQTT, and LoRaWAN. Our edge security solution, AI EdgeLabs supports these communication protocols for reliable communication between the devices and systems in the energy industry.
The energy industry suffers from financial losses in case of cyber attacks through deployed connected edge devices. AI EdgeLabs is a market-leading edge security solution with cutting-edge technology that incorporates AI capabilities for your distributed energy infrastructure.
If you want to learn more about our security solution, check out our ‘AI EdgeLabs security platform.’