The future of cyber security is constantly developing and changing. As new threats and vulnerabilities are discovered, businesses need to be prepared to protect themselves against these dangers.
Spending on cyber security is unlikely to slow down anytime soon. According to the International Data Corporation, global cyber security spending is expected to reach $174.7 billion in 2024, with security services being the most prominent and fastest-growing market category.
The future of cyber threat intelligence
Machine learning is artificial intelligence that allows computers to study data and learn its meaning. Combining machine learning and cyber threat intelligence enables computers to detect assaults faster than people can and stop them before they cause additional damage.
Here are some actions businesses could take to increase attack detection using AI threat intelligence platforms with machine learning capabilities.
Use just the best threat intelligence streams
Machine learning-based AI threat intelligence products collect inputs, analyse them, and provide outputs. Machine learning’s inputs for attack detection include threat intelligence, and its results are either notifications signalling assaults or automated actions that terminate attacks.
If the threat intelligence contains errors, it will provide “poor” information to the attack detection tools, resulting in “bad” outputs from the tools’ machine learning algorithms.
Many companies pay for threat intelligence from a variety of sources. These include feeds that contain machine-readable evidence of assaults, such as the IP addresses of computers launching attacks and malware’s file names.
Services often provide human-readable writing detailing the latest risks and are another threat intelligence source. Machine learning, but not services, can use feeds.
Internet communications will almost certainly become more secure in the years to come, especially with the eventual inception of quantum networks, which would render network-based threats obsolete.
Human mistake is one of the most persistent problems. Employees will continue to facilitate data loss, intentionally or unintentionally, and attackers will continue relying on social engineering techniques like phishing and business email intrusion.
Provide the context that machine learning needs to reduce false positives
If you’re using threat intelligence and machine learning to accomplish things like automatically preventing attacks, false positives are a significant risk. Mistakes will disrupt otherwise beneficial activity and may negatively affect operations.
Threat intelligence is only one component of risk assessment. Another aspect is comprehending contextual knowledge, which can help machine learning extract more value from threat intelligence. Assume that threat intelligence identifies a hostile external IP address.
Outgoing network traffic to that address from an internal database server may warrant a unique response than outgoing network traffic to the same address from a server that transmits files to subscribers daily.
Putting machine learning-enabled technologies into a monitor-only mode, where they recognise what’s wrong but stop nothing, is a typical teaching method. Humans review and validate the machine learning tool’s alarms, letting it know which is false. Machine learning can’t learn from its mistakes until it receives human feedback.
Businesses should concentrate first and foremost on how to secure their operations now. As cyberattacks get more sophisticated, it’s more important than ever to have a solid foundation of excellent cyber security behaviours and best practices.
It’s challenging to look at the calendar and forecast the future, especially in a complex and fast-paced industry like cyber security. However, by attempting to establish that foundation, you can position your company for long-term success as changes occur and new challenges emerge—whatever they may be.