Threat Detection and Response Moves Left of “Boom”

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 organizations will have integrated AI-driven threat intelligence systems by 2025, enhancing their ability to identify and mitigate threats before they manifest into major incidents.

This blog explores how threat detection and response will likely evolve over the next year, emphasizing the necessity of using AI-driven threat intelligence to fight fire with fire. This includes preemptive, early warning strategies, which emphasize proactive measures to identify and neutralize threats before they can inflict damage.

Predictions

Escalation of State-Sponsored Cyberattacks

Geopolitical tensions will likely fuel an increase in state-sponsored cyberattacks. As cyber warfare becomes a common tool in global conflicts, critical infrastructure, and intellectual property will become key targets. Organizations must prioritize threat intelligence and adopt proactive measures to defend against these attacks.

Strategic Incident Prevention and Response Planning with Early Warning

Organizations are increasingly focusing on early warning strategies to detect and prevent threats before they materialize. 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 organizations to prevent entire categories of similar attacks. For instance, many organizations 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 defenses. 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 organizations 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 minimize 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, by 2025, 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 organizations to stay ahead of fast-evolving adversaries with real-time, validated detections and highly adaptable detection strategies tailored to emerging attack vectors. This approach marks a critical advancement in SOC functionality, providing a proactive, scalable threat detection and response framework.

AI Arms Race in Cybersecurity

The race to leverage AI in cybersecurity continues, with threat actors and defenders alike deploying AI-driven systems. AI-powered tools will be essential for detecting and countering threats in real time, necessitating continuous input from real-world asset exposure data to maintain efficacy.

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 defenses 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 scrutinize 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, organizations can fortify their defenses against these pervasive threats.

Generative AI and Large Language Models

In 2025, large language models (LLMs), such as the forthcoming version of ChatGPT, are anticipated to exceed present levels of expertise, although they will still face challenges in achieving deep understanding. Despite the emergence of alternative technologies like state space models (SSMs) and liquid neural networks, LLMs are expected to retain their prominence owing to their extensive adoption and ongoing enhancements in capabilities. The achievement of Artificial General Intelligence (AGI) remains unlikely for 2025, yet the influence of LLMs on various sectors continues to grow.

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 utilize AI to tailor phishing attacks, weaponize 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 revolutionize this landscape, transforming these systems into the intelligent, responsive tools they were always envisioned to be. By utilizing AI for dynamic and adaptive defense strategies, SOAR can enhance its capabilities to automate complex threat detection, analysis, and response processes with unprecedented efficiency and precision. This evolution will realize the true potential of SOAR, establishing it as a critical component in contemporary cybersecurity defense frameworks. With AI-driven reasoning, organizations can achieve faster mean time to detect (MTTD) and mean time to respond (MTTR), streamlining incident response processes and bolstering overall threat management.

Conclusion

In the cybersecurity landscape 2025, organizations must adopt proactive measures and leverage AI-driven tools to stay ahead of evolving threats. By focusing on understanding your threat landscape,  early threat detection, integrating real-time intelligence, and employing cutting-edge technologies, businesses can fortify their defenses and ensure robust protection against cyber adversaries.