Has automation become a big part of cybersecurity?
Malicious actors are developing tools and technologies that expose companies to cyber threats from a range of sources that are both internal and external. Internal threats being data theft, unauthorized device access, a lack of security culture, insufficient employee knowledge, etc. and external threats being vulnerabilities and malware such as viruses, worms, etc. For companies that want to protect their IT assets from internal and external threats related to information security, infrastructure, project management, and business continuity, automation is critical.
In the current security landscape, the internet is the source that organizations depend upon to set up infrastructure, develop a business model & improve the overall productivity of the company. However, with the rise & complexity of cyber attacks, it has become necessary to have countermeasures to reap the benefits of security implementations. Cyberattacks today have mostly become automated. If a company or even, for that matter, an individual attempts to protect against these attacks manually, it becomes a battle between man vs. machine (a "Terminator" reference) with the odds stacked against the company or individual. To adequately protect against malicious software, it is critical to fighting fire with fire, or as in this case, a machine with a machine, by incorporating automation into cybersecurity efforts.
Automation changes things by reducing the number of threats and allowing for quicker identification and avoidance of previously unknown threats. Many security vendors consider automation to improve efficiency and reduce the workforce. Automation should be regarded as making the human process faster, enforce protection, and predict better actions of an intended or unintended threat. Automation in many ways can act as a corrective or deterrent control when applied with the right resources. Here are a few ways automation can be used:
Gather and Correlate Data
Many security companies gather large quantities of threat intelligence. Data, on the other hand, is worthless unless it is structured into actionable measures. To do so effectively, companies must first obtain threat data from all attack vectors and data security within their systems and security risk information from outside sources. Inside the vast quantities of data, they must find groups of threats that behave similarly and use that information to predict the attacker's next move. The more data collected, the more reliable the findings, and the less likely the groups would identify an anomaly. As a result, the study must scale the existing threat volume, which is difficult to do manually. Data sequencing becomes quicker, more efficient, and more accurate thanks to machine learning and automation. The only way to effectively identify advanced and unfamiliar threats is to combine this strategy with complex threat analysis.
The Defensive Race (Protection)
When a threat has been detected, defenses must be implemented and deployed as soon as possible before an attack spreads through the organization's networks, endpoints, or cloud. Since analysis adds a time penalty, the best way to avoid a recently found attack is at the attack's expected next stage, not where it was detected. Manually developing a complete collection of protections for the various security technologies and compliance points capable of countering potential practices is a time-consuming process that is sluggish and challenging when correlating multiple security vendors in your environment not choosing the proper control and resources. While simultaneously maintaining with the attack, automation will help speed up the development of defenses without putting a strain on resources.
The Offensive Race (Implementation)
Once defenses have been established, they must be placed to avoid the attack from advancing further in its lifecycle. To provide adequate security against the attack's current and potential activities, protections should be implemented in the area where the threat was discovered and across all technology within the company. The only way to outrun and combat an automated and counterattack is to automate the delivery of defenses. You can predict the next phase of an unexpected attack more accurately and move quickly enough to avoid it with advanced extensive data attack profiling and automated generation and distribution of defenses.
Detect Infections Already in the Network and Systems
It is not long before a threat reaches the network, and it is a breach. It would help if you moved much faster than the attacker to avoid an attack before data escapes the network. You must be able to interpret data from your network forward and backward in time, searching for a set of behaviors that indicates a host in your network has been affected, in order to detect an infected host or inappropriate behaviors. Manually correlating and analyzing data around the network, endpoints, and clouds can be challenging to scale, much like analyzing unknown threats struggling to reach the platform.
Automation can lead to better identification and intervention before a target is breached. Malicious actors are constantly using automation to deploy security threats at a rapid rate. It is a constant battle between the one trying to exploit and the one's trying to defend. For automation to succeed, it must handle tomorrow's attacks, not just yesterday's.
