Tracing Threats
As software become more sophisticated, it is an unfortunate side effect that the threats also become more invasive and damaging. The signature indicators used by traditional security software are about 90% effective in apprehending recognised attacks but are essentially sitting ducks when it comes to new variants. AI would be the more viable option. Many specialists suggest the combination of traditional and AI analyses to be more productive.
Better Control over Vulnerabilities
The older school of cyber-security measures include compiling databases of vulnerabilities against which an organisation’s networks are compared. These points of vulnerability are increasing at a rapid rate. Over 20,000 new vulnerabilities were reported in 2019, which was 17.8% more than that of 2018. Considering the rates at which vulnerabilities are increasing, we are in of AI’s powers to track behavioural patterns, analyse them and protect an organisation before the vulnerabilities get exploited.
Protecting hardware
AI also has the bandwidth to monitor hardware performance and keep track of essential processes like backup power, cooling filters, power consumption, internal temperatures, bandwidth usage and so on. By doing so, it can assess hardware and infrastructure vulnerabilities and help reduce the cost of maintenance.
Controlling Phishing
AI can also be more effective in controlling phishing. Phishing is the process of setting up fake websites or sending fraudulent communication from what appears to be reputed sources. AI can assist in weeding out these websites and protect your login credentials or payment information from being exploited.
Better Security
By using AI powered machines, organisations can incorporate better security and authentication systems to safeguard their data. Instead of going the standard login-password route, biometrics such as facial or retina identification can be used to make firewalls stronger. While passcodes can be easily hacked, it is not the case with biometric authentication.
With AI, companies can also have better control over network security. The machine can learn typical network traffic patterns and isolate anomalies at the earliest. It can also recommend security policy and group workloads more effectively.