Artificial intelligence (AI) is not a new concept or technology in the world of cybersecurity. It’s been iterated and utilized by security professionals for decades. But as both cybersecurity technology and cyber threats continue to advance, AI is being adopted by organizations at a rapid pace, all of whom seek to harness AI’s power to automate, advance, and empower their security.
AI is advancing quickly and has the potential to advance many areas of cybersecurity, but like technologies that have come before it, AI is not a magic tool that will solve every problem and stop every threat actor who arrives at the gates of your organization’s network. It’s vital that organizations understand the role AI can play in their cybersecurity strategy, its limitations, and how it’s transforming threat detection and response.
What Role Does AI Play in Cybersecurity?
AI plays an increasingly important role in cybersecurity by helping organizations rapidly detect, respond to, analyze, and mitigate threats within their environments.
AI dates to the 1980s and evolved alongside other cybersecurity technology as well as changes in threat actor tactics, techniques, and procedures (TTPs). In the 1980s and 1990s, threat detection and response depended on signature-based methods (as we’ll explain below). But with advances in AI and machine learning (ML) — which allows systems to learn from data without being directly programmed — security has improved through more accurate behavior-based detection and better threat intelligence. This technology became more common in enterprise security tools and solutions starting in the 2010s, and now a newer term has emerged to encompass all that AI has to offer: AI-powered cybersecurity.
AI-powered cybersecurity refers to the use of AI within cybersecurity solutions, creating conditions for these solutions to be faster, smarter, more adaptable, and more precise in their key functions. While AI is not ready to replace all human security analysts, nor is it wise to fully automate your security in a world where threat actors are increasingly agile and intelligent, AI is rapidly transforming how cybersecurity operates.
How is AI-Powered Cybersecurity Different from Traditional Security Procedures?
The reason AI is so prevalent and important to the success of cybersecurity comes down to the same action that fuels its capabilities: behavior.
Before AI advanced to where it is today, cybersecurity relied solely on rule-based, or signature-based, threat detection techniques. Rule-based solutions alert only on known threats – malicious code or actions that have been previously identified and entered into the solution’s rule set.
For example , a rule-based solution is designed and configured to alert anytime “X” action occurs, or when any code matching “Y” signature appears. If the solution detects “X” or “Y,” an alert will be generated. But the solution can only identify “known bad” files and actions based on its signature database. What happens when a threat actor makes a move or launches a new kind of malware with a signature that’s not in the rule set? The solution won’t register the anomaly as a threat, and it will go undetected.
This proved to be a major security gap. Threat actors realized it was easy to change the most microscopic features of the malware or ransomware they deploy, thereby giving an existing, identified strain a new signature, enabling it to bypass security solutions looking for outdated versions of the strain.
While signature-based solutions continue to play a large role in threat detection, relying solely on this approach can cause drawbacks, such as potentially creating a high volume of false positives, alert noise, and subsequent alert fatigue on security analysts. This hampers an organization’s ability to both see and respond to threats swiftly.
As cyber threats advance, behavior-based threat detection has become far more effective, and, as a result, has been displacing outdated signature-based and rule-based detection methods. Behavior-based capabilities involve identifying malicious or anomalous actions, not using signatures, but rather a variety of techniques that identify anything new or different versus a baseline of normal activity in each environment.
This is where AI can be a force multiplier. AI promises to further advance behavior-based detection; it thrives in understanding behaviors and turning them into patterns for precise identification and detection, which allows AI to greatly enhance security capabilities.
Predictive AI models are being designed and used to learn the patterns of known threat actors, as well as historical and emerging malware and ransomware strains. If a threat actor deploys new malware with a slightly altered signature, an AI model will still recognize it if the strain follows a similar behavioral pattern of other, previously known and deployed malware strains.
Learn more about the role of AI in cyber attacks.
AI can, at scale, decipher usual from unusual user behavior and access events with extreme precision. AI models are also constantly learning and evolving due to the introduction of new data sets and human training, creating conditions for improved detection and alerting. AI can also, simply, conduct analysis and reach conclusions faster than humans, which in turn offers many potential benefits: reducing alert noise, increasing filtration of alerts, and providing precise, accurate information to security analysts.
Key Applications and Benefits of AI in Cybersecurity
AI can be incredibly powerful when it comes to detection and response. Being capable of not only detecting behaviors at scale but also deciphering the details of previously unknown behaviors increases the capabilities of any detection and response solution, while creating efficiencies for the security teams that manage it.
Other key applications for AI in cybersecurity include:
- Predictive threat detection
- Malware-specific detections
- Threat intelligence correlation
- Network traffic analysis
- Intrusion prevention systems
- Automated threat and incident response actions
- Predictive analysis
- Continuous behavior and detection learning
- Automation of security operations
All these capabilities work together to deliver meaningful, measurable benefits for organizations that face challenges in responding to threats, maintaining in-house security expertise, and implementing the right technologies to meet both business and security goals.
Benefits of utilizing AI in cybersecurity include:
- Faster threat detection and response
- Broader and more effective data correlation
- More efficient, streamlined security operations
- Reduced reliance on individual personnel for critical security tasks
- Improved readiness for threats and incident response
- Elimination of long-standing, organization-specific security gaps
How is AI Changing Cybersecurity in Terms of Detection and Response
While AI has a wide range of applications in cybersecurity, from enhancing threat intelligence gathering, correlation, and analysis, to automating tasks that may burden security teams, where the technology has begun to truly shine is with its detection and response capabilities.
AI’s enhancement to detection and response solutions is due to a few factors, such as:
1. AI is constantly learning, meaning it is always taking in and analyzing new information to make better decisions and act with more precision.
2. AI can take in and correlate historical data alongside new data. For example, the entire history of cybercrime can be “taught” to an AI solution . This means AI models can absorb and understand near-endless amounts of information about various malware strains, threat actor groups, common indicators of comprise (IOCs), new tactics and techniques, and more.
3. AI can operate at a much faster and larger scale than any human could hope to achieve, ingesting and analyzing millions of data points, events, and detections within a short time frame.
All three of these factors multiply the capabilities of modern detection and response solutions by providing more precise, more accurate, and faster alerting for security teams, and by reducing false positive rates while ensuring anomalies are caught in near-real time.
However, AI is only as capable as the humans programming it. AI models need constant attention, fine-tuning, and oversight from data science experts. There’s a major difference between integrating the powers of AI into a proven detection and response solution, and relying solely on AI. The latter isn’t advisable or even possible at the present time. Indeed, handing off entire security functions to an AI engine can hinder your detection and response capabilities more than enhance them.
In the same way that traditional rules-based detection and response systems needed fine-tuning and augmentation from human expertise, the best cybersecurity solutions will almost certainly come from solutions that integrate AI to improve existing human-defined and -backed functions, versus relying solely on AI devices.
AI is not threat-proof either. Learn more about securing your AI technology.
Arctic Wolf: AI-Powered Security Operations
Arctic Wolf has long harnessed the power of AI to improve detections, increase operational efficiency, and help organizations around the globe End Cyber Risk®.
Arctic Wolf’s Alpha AI autonomously prevents advanced threats and automates labor- intensive workflows. Arctic Wolf utilizes Alpha AI within our full suite of security solutions, providing faster time to value, increasing cyber resilience, providing a more precise review of anomalous activity, and increasing analyst efficiency.
Learn more about Alpha AI.
Explore how AI can transform your security operations with our on-demand webinar, “Arctic Wolf Aurora Summit: AI, Adversaries, and a New Dawn for Cybersecurity.”
