Securing government communications in the era of hybrid work
The rise of hybrid work in the wake of the COVID-19 pandemic has given employees more freedom to choose how and where they work. But it has also presented challenges for public sector entities needing to keep organisational networks secure while giving distributed team members the ability to collaborate remotely.
While some industries are more accountable than others when it comes to securing networks and the exchange of information from one distributed team member to another, nowhere is the protection of internal information and communications more important than in the government sector.
Government organisations of all tiers are subject to some of the most stringent information security policies of any other industry segment in Australia, and for good reason. Government entities deal with information that may have a bearing on national security, citizen services and major projects, among other areas that can impact the country’s institutions and its people.
As a result, government organisations can be prominent targets for attack. With this in mind, it should come as little surprise that when it comes to enabling government workers to communicate and collaborate with colleagues from home, government sector organisations must take particular precautions to keep potentially sensitive information safe and secure.
Ensuring security at the edge
As any IT professional will tell you, a big part of keeping information safe and secure is ensuring it is not intercepted when travelling from one place to another on an organisational network. And in the era of remote work, organisational networks are very broad indeed, extending to workers’ homes and any number of other locations beyond the office.
Simply reducing the number of waypoints that such information needs to travel through to get from one remote worker, into the office and on to other remote locations can help to minimise the risks of data being intercepted by bad actors. This is where artificial intelligence at the edge can come into play.
AI at the edge — sometimes referred to as ‘edge AI’ — refers to any AI-based software models that can run and process information on hardware devices in the field (or in the home), at the very ‘edge’ of organisational networks, independent of cloud services or infrastructure, which is where a lot of AI processing gets done today.
Edge AI may not be as well known as generative AI technology like that offered by ChatGPT, but it also has a lot to offer. The main benefit of edge AI is that it relies solely on the processing power of local devices, so it doesn’t need to send information to and from cloud servers in faraway places to analyse and manipulate data. This makes it quick and secure — two factors that are particularly useful in the area of video collaboration.
Securing the virtual workplace
As we all know, videoconferencing and remote collaboration are core tools of business in the post-COVID hybrid work era, including in the government sector. But challenges arise when entire workforces are using platforms to facilitate remote collaboration. For most businesses, cloud capacity and internet bandwidth are among the most prominent challenges. In the government realm, however, it is security that often takes the top spot in terms of priority.
This is why edge AI should be of particular interest to government entities with distributed hybrid teams. Typically, devices such as cameras and microphones used for virtual meetings held via cloud-based collaboration platforms do not process incoming or outgoing data locally, on their own hardware, for purposes such as analytics, background blurring or speaker identification. This means raw data is usually sent to a cloud server for treatment before it lands at the office.
The additional transfer points that such processes entail put data streams and potentially sensitive information at greater risk of being intercepted. However, devices enabled by edge AI have the capability to process data before it is sent over the internet and across distributed organisational networks. Indeed, data can be anonymised before it ever leaves the device.
In this way, among others, video devices enabled by edge AI have the potential to minimise the number of transfer points at which potentially sensitive government data could be intercepted by malicious actors. This capability allows users to benefit from advanced video analytics functions without the fear of what is happening with their data.
Prioritising protection and performance at home
Because edge AI technology can conduct complex and in-depth processing on hybrid workers’ devices, it can also be used to detect and block potential cyber threats, such as malware or unauthorised access to a video collaboration system, at the edge in the home.
Response time is a key factor in the effectiveness of cyber risk responses, and having devices at the edge taking on such duties can deliver a boost in response time — a handy capability for government entities required to adhere to stringent security policies and practices.
At the same time, when raw data is processed at the edge of the network, on hybrid workers’ devices, it reduces the need to send large quantities of video or audio data to the cloud or other data centre environments to be processed. Indeed, edge AI software has the potential to compress video data before it is ever transmitted, reducing the amount of bandwidth required for remote communications.
At the same time, because edge AI reduces the need for data to be sent to the cloud for processing before it continues on to its next destination, it can dramatically reduce latency — the time it takes for data to get from one place to another.
At the very least, edge AI has the potential to make government workers’ remote collaboration experiences smoother and more enjoyable. More importantly, it can provide a more secure distributed network for government organisations trying to keep their teams connected from afar.
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