Although the number of security threats lurking around your application is always growing, the number of tools designed to combat them is too. Cyber security solution providers are increasingly offering AI-driven tools as part of their solutions. These tools have been around for a long time, but recent technological developments have improved their ability to automatically detect and respond to threats. This makes them excellent tools for improving both your application security and your incident responses.
The Rise of AI in Security
For many cybersecurity providers, artificial intelligence is nothing new. Providers have been using AI and machine learning algorithms since the 1990s to improve their threat detection capabilities and reduce response times. However, the explosive growth of AI in recent years has expanded usage and capabilities.
Cybersecurity providers are in a much better position to address threats with new (and much better) algorithms as well as new types of machine learning. Natural Language Processing (NLP), for example, is a relatively recent development that uses statistical analysis and linguistic research to translate between machine language and human language. NLP has been a game-changer for cybersecurity, especially for threat detection and response solutions.
Presently, AI and machine learning have a few critical advantages:
- Big data. There is more data available than ever before. This means that machine learning algorithms are able to train on massive data sets, making them more precise and effective at differentiating between authentic and unauthorized traffic.
- Increased computational power. Compared to the processors available in 1995, processors now are exponentially more powerful. This allows security tools to integrate AI without significantly slowing down an application or machine.
- Adaptability. Because machine learning occurs in real time, algorithms are able to respond with more accuracy to potential threats. Even if a security solution is programmed with preset rules for what a threat should look like, AI and machine learning will identify the threat in part based on behavior analysis and patterns. So, the rules can be updated as needed, and threat detection is better.
While traditional security tools can be effective, more organizations are adopting tools that use AI. Many attackers are launching increasingly subtle and sophisticated attacks that tend to evade detection. To prevent these attacks from slipping through your defenses, it’s best to implement the most effective solutions, and AI is the tool in the best position to identify these novel, highly evasive threats.
AI-Powered Threat Detection Capabilities
Most security teams are overwhelmed by the number of threats and vulnerabilities that they need to address at any given time.
This problem often causes proactive threat detection to be deprioritized, which is ultimately a problem for security. One important facet of AI-powered security solutions, then, is their capacity for unsupervised learning. Rather than requiring security professionals to invest time and resources into customizing and setting up the solution, machine learning can be leveraged to independently learn to detect anomalies.
This is very valuable to security teams as they are able to focus their efforts on other priorities. However, independence is not the only feature that AI and machine learning bring to the table. Because the tool learns on large data sets and learns behavior patterns that may elude the human eye, threat detection capabilities are often better than those of traditional tools.
This is especially useful for combating zero-day attacks. Traditional detection methods are less adept at picking up on unknown attack patterns. AI-driven tools are able to use predictive analytics based on their large data sets to detect and alert security teams to emerging threats.
Automated Incident Response and Remediation
A critical component of a successful incident response is a short response time. As a rule, it’s best to minimize the time between when an attack succeeds and when the attack is discovered and addressed. By monitoring activity and detecting threats as they emerge, AI-driven solutions can decrease response times.
However, AI and machine learning offer other benefits that can help an organization improve its incident response, including:
- Triage and prioritization. By comparing the activity and user behaviors in a current threat to previous attacks, AI-driven security solutions can quickly assess the risk level of a potential threat. They will alert security teams to the highest-priority issues to ensure that threats are addressed as quickly as possible and that no high-risk threats slip through the cracks.
- Orchestrated response actions. To further decrease response times, an effective solution may use SIEM and machine learning together. This streamlines the response process by alerting security teams, as a SIEM will, but also by automatically responding when possible. Not all threats can be neutralized automatically. However, for the threats that can be, the quick response reduces downtime and improves outcomes after incidents.
Quick, effective remediation is an important part of limiting the impact of an incident. Using AI-driven cybersecurity solutions that leverage machine learning can improve your organization’s chances of recovering well from an incident. In many cases, these solutions can prevent incidents entirely.
Although AI has been used in security for decades, recent improvements have made it a highly valuable asset for security teams. By automating alerts, prioritization, and some responses, AI tools improve threat detection and remediation.
These tools also allow security professionals to focus their limited time and resources on other projects that are more important to achieving your organization’s strategic goals. Because of their efficacy and speed, AI-driven tools and their providers are quickly transforming organizations’ approaches to threat detection and response.
