The world of hybrid security with AI is a fascinating topic. It represents the intersection of traditional cybersecurity methods and cutting-edge AI technologies. This combination leverages the strengths of both approaches to create a more robust and adaptive defense strategy.
In this post, we're going to explore how the hype about AI can sometimes be misinterpreted by clients and businesses, but actually, it's a beneficial trend in the long run. I'll introduce some new terminology because today’s modern businesses that I consult are already adapting to this new way of thinking and engineering applications. In systems that are secure by design, AI fills the gaps effectively.
Whether you're a CTO, security analyst, tech enthusiast, or just someone interested in keeping your digital life safe, this AI for security post is for you.
What Is Hybrid Security?
Hybrid security is a blend of traditional security measures, like firewalls, logs, application security controls, scanners, and antivirus software, now enhanced by the power of AI, particularly large language models (LLMs).
These pre-trained models excel at sophisticated tasks such as log analysis, anomaly detection, categorization, and preventing attacks. Think of hybrid security as combining the best of both worlds. Traditional security methods are effective at handling known threats but can struggle with the more sophisticated attacks we see today.
This is where AI steps in to fill the gap. Humans, while capable of making informed decisions, perform slowly when it comes to processing large amounts of data and multitasking. On the other hand, AI can parse data quickly but struggles with decision-making and seeing the bigger picture.
By combining human expertise with AI tools, we create a highly efficient ethical hacker. This hybrid security approach leverages AI's computational superiority while allowing humans to make informed decisions based on refined data and experience. This collaboration protects businesses, products, and clients. It can help leaders deliver secure software applications and achieve greater resilience against cyber threats.
Today’s AI brings several key advantages to the table:
- Anomaly Detection: Traditional systems rely on set rules and perform well, but AI learns what "normal" patterns look like and can spot unusual activity that might signal a security breach. This is powerful because, despite occasionally reporting false-positive alerts, AI can detect subtle issues that humans might miss. Our goal is not perfection but to be informed with refined data so that we, as humans, can make the final conclusions.
- Predictive Analysis: AI can parse and categorize massive amounts of data, identifying potential threats before they become problematic, which gives you a proactive edge. While security experts make predictions based on technical experience, the reality is that no one can master all technologies simultaneously. This cognitive limitation is where AI steps in to help us comprehend and predict possible scenarios.
- Automation: AI can handle routine security tasks and free up your team to focus on more complex issues. This change can motivate security experts to research new threats instead of performing mundane tasks, which can lead to better security strategies and proactive threat management.
- Monitoring Traffic: AI can analyze traffic patterns to detect unusual activities, such as potential data breaches or unauthorized access attempts. By monitoring network traffic 24/7, AI can identify deviations from established baselines. For example, an unusual spike in data transfer or access attempts from unfamiliar locations can be flagged as suspicious.
- Vulnerability Scanning: Regular scans, enhanced by AI, can identify and prioritize vulnerabilities in applications and APIs. AI-powered vulnerability scanners can continuously assess the security posture of systems, identifying weaknesses that traditional methods might overlook. These scanners use machine learning to understand the context of each vulnerability, which allows them to prioritize based on the potential impact and exploitability.
- Automated Response: In the event of a detected threat, automated responses can immediately neutralize or contain the issue to reduce the risk of damage or data loss. AI can be programmed to execute predefined response protocols, such as isolating affected systems, blocking malicious IP addresses, or initiating data backups. This rapid response capability is crucial in mitigating the effects of an attack and ensuring business continuity.
How Reliable Is AI in Making Security Decisions?
You might be wondering, "How reliable is AI in security? Is it going to replace people?" It's a great question and a fair concern. AI has shown impressive results in detecting and responding to threats, but it's not perfect.
More importantly, humans are not developing it to be perfect. AI will always be used as an assistive tool that enhances productivity and refines information within a specific context.
From my personal experience, we should not rely entirely on AI inputs and outputs, because what no one talks about is the long-term psychological impact on us.
In theory, if a security expert relies 100% on AI, they will eventually lose the skillset, motivation, and drive that made them great. They may become outdated or incapable of protecting systems that AI might not understand. However, I hope that security experts who read this article are like me—they will never stop learning and evolving, AI or not.
What we should also consider before relying on AI are the following technical aspects:
Accuracy and Bias
The reliability of AI in cyber security depends on the data it's trained on. If the data is biased or incomplete, the AI might make mistakes.
Another tricky aspect is that AI can be misconfigured to interpret correct data and output conclusions that are logical but incorrect. Security experts should maintain a clear mind and resist the temptation to believe something that merely looks logical. They should have their own ways of verifying information.
False Positives / Negatives
AI can sometimes mistakenly flag safe activities as threats (false positives) or miss real threats (false negatives). However, with continuous learning, these systems improve over time.
It's important for humans to "provoke" and "train" the system, especially if it is based on unsupervised learning, to help it evolve. Regular updates and feedback loops are essential to fine-tune AI performance and reliability.
Adaptability
One of AI's biggest strengths is its ability to adapt. Unlike traditional systems, AI can evolve with the threat landscape, which makes it a dynamic defense tool. Security experts also adapt, but they should recognize the limitations in time.
AI can quickly learn from new data and adjust its algorithms to address emerging threats. This adaptability allows AI to stay ahead of cybercriminals and provide robust security solutions in an ever-changing digital environment.
How Businesses Can Optimize Their Security Processes Using AI
Businesses can greatly benefit from integrating AI into their security strategies, applications, and processes. However, each business should carefully evaluate if integration is truly necessary and in which areas, rather than simply following the hype because it's trendy or because "everybody does it." A mature business should view AI as a tool that can improve efficiency without introducing unnecessary complexity.
In a security context, here are a few ways AI can typically help:
- Enhanced Monitoring: AI can monitor your network or application to catch any suspicious behavior as it happens. This leads to faster detection and response times, as concrete information results in concrete actions. By identifying anomalies and deviations from normal patterns in real-time, AI enables proactive threat mitigation. This constant vigilance reduces the risk of undetected breaches and ensures that security measures are always up-to-date.
- Incident Response: When it comes to handling incidents, speed is everything. Automating responses to common threats means they can be contained and resolved quickly. AI tools, like heuristic analyzers, make backup a breeze. If an incident occurs, AI can pinpoint what changed and restore the corrupted data from a clean backup.
- Resource Allocation: Not all threats are created equal. AI helps prioritize them, handling some of the workload so your team can focus on the most critical issues first. By analyzing the severity and potential impact of detected threats, AI ensures that resources are used efficiently and the most pressing concerns are tackled promptly.
- Compliance and Reporting: Meeting regulatory requirements can be a hassle, but AI makes it easier by automating data collection and reporting. It monitors and documents security activities to make sure everything is recorded accurately. This automation lightens the load on your security team, reduces human error, and ensures timely, precise reporting to regulatory bodies.
Figure 1 - Research-based diagram of productivity
Conclusion
Hybrid security with AI is a game-changer in the world of cybersecurity. By combining traditional security methods with the adaptive and automated capabilities of AI, businesses can enhance their defenses and streamline their operations. This approach saves time, which is often the most costly resource. While AI isn't a silver bullet, its benefits in terms of adaptability, automation, and proactive threat detection make it a valuable addition to any AI security strategy.
However, AI can sometimes complicate processes that work just fine using traditional methods. Each business must evaluate when AI adds value to a situation and when it introduces unnecessary complexity. For some businesses, AI can potentially cause more harm than good. The best way to determine its value is to address a specific challenge with a proof of concept (POC), which will demonstrate whether AI handles the challenge better or worse.
LLMs and AI technologies excel at finding patterns and processing large, correlated data sets. They often imitate certain tasks so successfully that we might think they are "alive" or "thinking." However, a side effect of this is that humans might become overly reliant on these tools, potentially downgrading the quality of human-delivered work, which can be detrimental to the business in the long run.
