Today, automation has become indispensable in almost all business-critical processes, for example, to minimize costs, plan resources better or increase efficiency. Automation has also been used in cyber security to protect against new and complex cyber-attacks.
In the future, however, this will no longer be sufficient. With the increasing digitization of business processes, holistic security automation is essential to protect against cyber threats. When you look at the traditional approach to identifying behavioral anomalies and threat indicators, you can see why automation should be an essential building block of any security strategy. To protect a system, the threat must first be identified. In other words, the attack surface must be constantly monitored. In real-time – or as close to it as possible – data streams flow to a central system such as the External Data Representation ( XDR ), which normalizes and aggregates the data to search for signs of a threat. These can be very diverse – a specific malware-Signature, a suspicious pattern of behavior or a series of events that are individually harmless but combine to indicate a sophisticated attack. For the XDR to know what it is looking for, it needs to know what characteristics mean an attack. This requires an IT team to proactively analyze this threat intelligence and use tools to detect threat patterns and uncover connections to identify new episodes and initiate countermeasures.
Given the ever-expanding company networks and the simultaneous cost savings, such an approach is hardly sustainable in many security teams. Due to the size of the attack surface in companies, a manual search is a futile task that is also slow and error-prone. Technologies such as Robotic Process Automation (RPA) have been used for some time. Their use allows aggregating and extracting data while performing basic threat scanning and detection. The focus of RPA is on standardized and repetitive processes that have a high workload so that employees can take care of more complex tasks. According to Gartner, for more critical security operations that may require more increased analytical capabilities, security leaders use end-to-end automation that goes beyond RPA and leverages complementary technologies to handle more complex tasks.
Application Of Hyper Automation In Cybersecurity
Therefore, the next step towards comprehensive protection is the hyper-automation of the security processes. The combination of advanced technologies such as artificial intelligence (AI), machine learning (ML), process mining and robotic process automation (RPA) offers completely new potential here, Automate, accelerate and improve security responses. The interaction of the technologies makes it possible to automate where automation was not possible before: with previously undocumented processes that rely on unstructured data entry.
For cybersecurity, this means, among other things:
- Automation and machine learning enables faster and more efficient sequencing of large volumes of threat intelligence collected on-premises and from data collected from external sources. Improved environmental monitoring – i.e. vulnerability and device usage analysis – reduces the likelihood that anomalies will be false alarms. In combination with dynamic threat analysis, unknown threats can also be detected.
- Whether fully automated or still dependent on human involvement, Responses are implemented faster and more accurately. The best possible protection of the network can be achieved with the help of risk-based prioritization of patching updates. By using machine learning and predictive analytics, IT can see which vulnerabilities can be exploited and, based on priorities, take action to combat the threat.
- Even if the network is already compromised, automation can provide faster analysis, detection, and response. Identifying an infected host or suspicious behavior requires analyzing data and looking for a combination of behaviors that indicate a host in the environment has been infected. Manually correlating all data across the network, endpoints, and clouds would be far too slow.
Cybercriminals use automation to deploy new threats at ever shorter intervals. Therefore, leading security departments use automation themselves as part of their security strategy to efficiently fend off attacks.
Also Read: Artificial Intelligence Aims To Make Agriculture More Ecological