Smarter Cybersecurity Starts with AI
New Tech Tuesdays
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Published July 22, 2025
Cyberattacks are not simply increasing; they are getting smarter. Traditional security tools that use rule-based systems are no longer fast or flexible enough to handle modern attacks.[1] These systems work well with known vulnerabilities but can miss new and evolving attacks. To stay ahead, engineers and security professionals are working on solutions that can adapt quickly. As part of this effort, artificial intelligence (AI) and machine learning (ML) are becoming important tools in fighting today's threats.
Utilizing AI to enhance cybersecurity means detecting threats as they happen and responding before damage occurs. In this week’s New Tech Tuesdays, we examine how AI and ML are reshaping cybersecurity into an intuitive force through real-time intrusion detection, malware analysis, and automated defensive responses.
How AI Detects Threats in Real Time
Legacy rule-based systems rely on known attack signatures and predefined rules, but AI-powered tools excel where these systems fall short. Using ML, they can scan large amounts of network traffic and flag unusual behavior as it happens. These ML models adapt to new threats by learning from past data and evolving their detection capabilities.
For example, an AI-powered intrusion detection system (IDS) does not rely on static rules. Instead, newer tools use ML to look for unusual behavior, such as unexpected login attempts or irregular traffic patterns, based on evolving network data (Figure 1). This approach helps catch growing threats and lowers the number of false alarms.
Figure 1: AI-powered systems analyze network traffic in real time, helping identify unusual patterns that may signal emerging threats. (Source: Johnnii/stock.adobe.com; generated with AI)
Cybersecurity company Darktrace uses unsupervised ML to model the normal "pattern of life" within a network.[2] Their AI platform then identifies and responds to any deviation, often catching threats before human analysts even know about them.
AI can also help automate malware detection. While traditional malware detection relies on known file signatures, AI can leverage behavior-based models to see how a program behaves in a sandbox environment. AI-driven malware analysis can flag malicious software that conceals its code, spot obscure threats that bypass traditional filters, and continually adapt strategies based on real-world attack patterns.
AI-Driven Security Response
Detection is only half the battle. AI enables automated incident response that can act in milliseconds. These systems can automatically isolate affected endpoints, trigger patching workflows, and notify human workers of critical threats.[3]
In Internet of Things (IoT) networks where devices can be vulnerable, AI can instantly spot abnormal communication patterns and shut down compromised nodes before a breach spreads. For example, cybersecurity start-up Reco uses generative AI to monitor software-as-a-service (SaaS) applications in real time to identify unauthorized access and accelerate IT response times.[4]
Automation expedites containment and reduces the burden on security teams. With AI’s ability to accurately decipher deviations from normal network activity using evolving behavior-based models and predictive analysis, automated cybersecurity can proactively identify, report, contain, and defend against sophisticated network breaches.
Evolving Threats Need Evolving Hardware
Today, AI is improving defenses and preparing the industry for what comes next. Engineers are turning to hardware that can quickly process and respond to cyberthreats.[5] Edge AI processors allow for faster threat detection at the device level. Cryptographic integrated circuits (ICs) and secure elements help protect identity and data at the source, while field programmable gate arrays (FPGAs) and embedded accelerators are used in high-throughput applications like firewalls and secure gateways.
These components will set the stage for more autonomous defense systems that detect, respond, and adapt as threats do. As cyberattacks get more complex, so will the hardware designed to stop them.
The Newest Products for Your Newest Designs®
This week’s New Tech Tuesdays highlights an example of the hardware that will play a key role in shaping smarter cybersecurity systems: the STMicroelectronics STM32N6 high-performance microcontroller series. Well-suited for AI-based cybersecurity applications, these devices include Arm® Helium™ vector processing for energy-efficient digital signal processing. Additionally, these microcontrollers feature ST's Neural-ART Accelerator™, a neural processing unit (NPU) capable of up to 1GHz clock speed and 600 giga operations per second (GOPS), enabling real-time execution for computer vision and cryptographic applications. They also offer advanced security features, including Arm TrustZone® technology, Security Evaluation Standard for IoT Platforms (SESIP) Level 3 and Arm Professional Service Automation (PSA) Level 3 certifications, and side-channel resistant Advanced Encryption Standard/Public Key Algorithm (AES/PKA) and fast Advanced Encryption Standard/Secure Hash Algorithm (AES/SHA) cryptography.
Tuesday’s Takeaway
Security systems need to do more than just react; they need to adapt. AI helps ensure network health by assisting in real-time detection and automated responses, and engineers are thinking about how and where protection happens. This results in AI-powered cybersecurity systems that leverage predictive analysis, using historical data and threat intelligence to forecast potential attacks and implement preventative measures before they strike.
Sources
[1]https://arxiv.org/pdf/2308.05126
[2]https://www.darktrace.com/cyber-ai-glossary/darktrace-threat-detection
[3]https://intezer.com/blog/how-ai-transforms-incident-response/
[4]https://www.reco.ai/
[5]https://www.scaleoutsystems.com/edge-computing-and-ai