In 2024, advancements in artificial intelligence (AI) have led to increasingly sophisticated threat actor exploits, such as deepfake technology used in misinformation campaigns and AI-driven phishing attacks that mimic legitimate communications. As we approach 2025, significant transformations in the use of AI in threat detection, threat intelligence, and automated response/remediation will reshape the tools, strategies, and collaborative efforts used in combating sophisticated threat actors and their AI-powered attacks.
According to a recent report by Cybersecurity Ventures, there has been a 35% increase in the adoption of advanced threat detection tools among Fortune 500 companies. Also, Gartner predicts that 70% of organisations will have integrated AI-driven threat intelligence systems by 2025, enhancing their ability to identify and mitigate threats before they manifest into major incidents.
Threat detection and response is likely to evolve over the next year, emphasising the necessity of using AI-driven threat intelligence to fight fire with fire. This includes preemptive, early warning strategies, which emphasise proactive measures to identify and neutralise threats before they can inflict damage.
Strategic Incident Prevention and Response Planning with Early Warning
Organisations are increasingly focusing on early warning strategies to detect and prevent threats before they materialise. By leveraging actionable intelligence, they can proactively address common vulnerabilities, reducing the likelihood of attacks at their source. Identifying the root weaknesses behind these vulnerabilities and addressing them comprehensively allows organisations to prevent entire categories of similar attacks. For instance, many organisations employ multi-factor authentication (MFA) to prevent account takeover attacks, exemplifying a “left of boom” approach.
In military terms, “left of boom” refers to actions taken to disrupt adversary plans before an explosive event occurs. In cybersecurity, it signifies a proactive stance to detect and mitigate threats before they penetrate defences. Just as intelligence gathering is essential in military operations to foresee and thwart attacks, cyber threat intelligence plays a similar role in identifying potential weaknesses and threat vectors early on.
More organisations and government agencies will likely conduct internal tabletop exercises for various attack scenarios. These exercises and regularly updated incident response playbooks, will ensure preparedness against current threats. These proactive approaches will help minimise potential damage and speed recovery in the event of an attack.
Rise of Detection-as-Code
Today’s Security Operations Center (SOC) detections often lack robust validation for accuracy, resulting in limited effectiveness against real threats. This is largely due to the ad-hoc implementation of detection processes, where rules are hastily added to SIEM systems without rigorous testing. However, the widespread adoption of detection-as-code (DaC) is expected to transform SOC capabilities. This methodology will allow SOC teams to program, version control, and deploy detection logic with the precision and efficiency of continuous integration/continuous delivery (CI/CD) pipelines in software development.
DaC will empower SOCs to rapidly respond to evolving threats, enabling automated and continuous updates to detection rules aligned with the latest threat intelligence. Integrating CI/CD principles will allow for continuous testing of detection logic, reducing false positives and enhancing detection accuracy while fostering collaboration between security engineers and developers. Moreover, embedding AI within the detection pipeline will enhance the adaptive capabilities of SOCs, allowing for advanced threat detection and response. Ultimately, DaC will bring agility to SOC operations, enabling organisations to stay ahead of fast-evolving adversaries with real-time, validated detections and highly adaptable detection strategies tailored to emerging attack vectors.
Synthetic Data for AI Training
In 2025, the growing concerns around data privacy and regulatory constraints will drive a significant increase in the use of synthetic data for training AI models in cybersecurity. Synthetic data will enable AI systems to learn patterns, detect threats, and improve defences without accessing sensitive or personally identifiable information (PII). This approach ensures compliance with privacy laws like GDPR while allowing for robust AI-driven security measures to be developed.
Open Source Software Libraries
Open-source software libraries will remain a prime target for threat actors, as they are integral to many commercial and enterprise applications. The inherent transparency of these libraries offers attackers an accessible entry point to exploit vulnerabilities, insert malicious code, or compromise supply chains. As dependency on open-source components grows, securing these libraries becomes paramount. Threat actors persistently scrutinise popular libraries for weaknesses, using them as launchpads for widespread attacks. Consequently, ensuring software supply chain security is becoming an imperative priority for both developers and security professionals. By implementing rigorous assessment and monitoring strategies, organisations can fortify their defences against these pervasive threats.
Generative AI in Cybersecurity
Generative AI models are poised to play a critical role in cybersecurity for attackers and defenders. On the defensive front, these models will aid in crafting advanced playbooks, formulating security policies, generating test cases for security solutions, and streamlining processes such as patch management. Conversely, adversaries may harness generative AI to refine social engineering techniques or automate the development of malicious code. Cybercriminals could utilise AI to tailor phishing attacks, weaponise existing vulnerabilities, and create AI-driven malware that adapts dynamically to bypass security measures. Consequently, cybersecurity experts will require robust AI-powered tools to identify and counteract these evolving threats, underscoring the importance of staying ahead in the AI arms race to secure digital environments.
SOAR with AI: The Future of Cybersecurity Operations
The promise of SOAR (Security Orchestration, Automation, and Response) has been significant in streamlining cybersecurity operations. However, it has yet to fully deliver on its potential. The integration of AI into SOAR platforms promises to revolutionise this landscape, transforming these systems into the intelligent, responsive tools they were always envisioned to be. By utilising AI for dynamic and adaptive defence strategies, SOAR can enhance its capabilities to automate complex threat detection, analysis, and response processes with unprecedented efficiency and precision. This evolution will realise the true potential of SOAR, establishing it as a critical component in contemporary cybersecurity defence frameworks. With AI-driven reasoning, organisations can achieve faster mean time to detect (MTTD) and mean time to respond (MTTR), streamlining incident response processes and bolstering overall threat management.
In the cybersecurity landscape in 2025, organisations must adopt proactive measures and leverage AI-driven tools to stay ahead of evolving threats. By focusing on understanding and implementing early threat detection, real-time intelligence, and cutting-edge technologies, businesses can fortify their defences and ensure robust protection against cyber adversaries.
