For malicious actors, generative AI (gen AI) presents stealthier, more efficient, and increasingly effective methods for launching cyberattacks. Cybercriminals can now scan entire organizational data infrastructures, create highly convincing fake identities, and infiltrate critical IoT systems faster than ever.
In response, governments and businesses are strengthening their defenses. Across EMEA, cyber defenders are leveraging AI generation in innovative ways to counter these rapidly evolving threats. Their not-so-secret weapon? Real-time data streaming.
The combination of generative AI and stream processing is proving to be a powerful defense for organizations navigating this complex threat landscape. Here are three compelling examples that demonstrate how to do it.
1. Real-time threat detection and response
A real-time threat requires a real-time response. The quicker an organization can identify and respond to a breach, the lower its impact. Batch data processing, which analyzes stored data, is not enough when systems are actively under attack.
Real-time AI-powered generative analytics enable continuous monitoring of data flows across networks, enabling security systems to detect anomalies and potential threats as they occur.
Beyond detection, generative AI automates response processes throughout the security chain. From identifying the threat to isolating compromised systems, automation significantly reduces the time it takes to contain and resolve incidents. This also minimizes the risk of human error, preventing a panic response from causing further damage during an attack.
British telecommunications giant Vodafone has invested heavily in real-time threat detection and response systems as part of its wider cybersecurity strategy. Its internal global cybersecurity arm employs 900 people to manage billions of events and logs from sensors in every country in which it operates.
This advanced network monitoring allows Vodafone to gain real-time visibility into all data traffic, detecting anomalies such as usage spikes or unusual access patterns to identify threats before they occur. they cannot cause damage. AI and machine learning (ML) augment these efforts by detecting anomalous behavior, predicting potential vulnerabilities, and automating routine security tasks.
Incident response automation ensures that the organization responds quickly to detected threats. Automated AI and ML-powered playbooks and workflows trigger immediate actions, such as isolating affected devices or rerouting traffic during DDoS attacks. By automating responses and reducing human errors, Vodafone ensures rapid mitigation of cyber risks across its large and complex infrastructure.
2. Real-time behavioral analysis of insider threats
Behavioral analytics is another effective way that data streaming strengthens cybersecurity. By establishing a baseline of normal user and device behavior, organizations can more easily detect anomalies that signal potential threats. For example, compromised credentials can be flagged when a user’s actions deviate from their usual patterns. In the IoT space, device profiles can track normal resource usage, alerting security teams when unusual activity suggests a problem.
Both of these illustrate the critical need to process and analyze data in the flow so that information can be as immediate and accurate as possible to prevent fraud or device failures. These examples highlight the critical need for real-time data processing and analysis to generate immediate and accurate insights.
One area where behavioral analytics shines is in insider threat detection, especially in organizations dealing with high-risk information. Continuously monitoring user behavior against established profiles helps prevent intentional and accidental violations, reducing the risk of costly incidents.
For example, a bank could use AI to track the activities of its employees across its network. By analyzing login times, file access and data transfers, AI can detect suspicious behavior (such as an employee accessing sensitive files outside of normal working hours) and flag it for further investigation. investigation.
3. Real-time threat intelligence sharing to combat APTs
In EMEA, cybersecurity threats frequently cross national borders. As cyberattacks become more sophisticated, defenders must analyze larger and more complex data sets from a wider range of sources, making intergovernmental collaboration paramount.
Generative AI and data streaming technologies streamline this process by quickly correlating data from various sources, giving organizations a more comprehensive view of the growing threat landscape. Multiple organizations can collaborate simultaneously to detect, monitor and respond to cybersecurity threats in real time.
The European Union Agency for Cybersecurity (ENISA) plays a crucial role in promoting the sharing of real-time threat intelligence between Member States. The agency promotes collaboration through the European Cybersecurity Act and coordinates initiatives such as Cyber Threat Platforms (CTI), in which various sectors share critical data in real time to mitigate cyber threats.
In the case of advanced persistent threats (APTs) – sophisticated, long-term attacks often backed by nation-states or well-funded groups – combining generative AI with stream processing is key to spotting the needle in the haystack.
British cybersecurity company Darktrace has developed an AI-driven cybersecurity platform that leverages machine learning, including generational AI capabilities, to detect and respond to sophisticated APTs.
The platform uses self-learning AI models to detect abnormal behavior in real time, identifying subtle signs of APT over the long term, such as unusual network traffic or lateral movement (where cyber attackers move from one system to another to gain deeper access to critical information). infrastructure). Darktrace’s gen AI technology can also simulate potential APT scenarios, helping to anticipate new attacks.
In March 2024, Darktrace detected suspicious emails on a customer’s network, sent from addresses associated with a renowned international fast food chain. The attackers used trusted domains and hid malicious links in QR codes, attempting to evade traditional email security measures. Darktrace’s AI flagged the unusual behavior, scanned QR codes, and identified the threat before any compromise occurred.
This case highlights the changing nature of cybersecurity threats: what was once considered secure is now vulnerable. Organizations must work together to keep pace with the evolution of cybercrime.
Strengthening defenses with advances in AI
The combined potential of generative AI and data streaming is something I find more exciting than intimidating. Companies are already seeing significant returns on their data streaming investmentsthereby improving operational efficiency, customer experience and accelerating AI/ML adoption.
However, these tools can be used by both attackers and defenders. To stay ahead, we must invest in the right infrastructure, talent and expertise to ensure we are on the winning side.
