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Cyber AI Chronicle
By Simon Ganiere · 12th January 2025
Welcome back!
Project Overwatch is a cutting-edge newsletter at the intersection of cybersecurity, AI, technology, and resilience, designed to navigate the complexities of our rapidly evolving digital landscape. It delivers insightful analysis and actionable intelligence, empowering you to stay ahead in a world where staying informed is not just an option, but a necessity.
Table of Contents
What I learned this week
TL;DR
Happy new year to everyone! Wishing you a successful, healthy, fun and loving 2025!
As the cybersecurity landscape evolves, so too must our strategies for navigating it. The dawn of 2025 brings both familiar challenges and emerging threats, underscored by rapid advancements in AI, quantum computing, and increasingly complex supply chains. Drawing from an extensive review of 25 vendor reports, I’ve synthesized these predictions into actionable insights, spotlighting a threat-based approach that prioritizes foundational controls and targeted responses. This article isn’t just about forecasting risks—it’s about transforming these insights into tangible actions that organizations can implement to stay resilient in an ever-changing environment. » READ MORE
While Christmas was not disrupted by major cyber events (I still remember that eventful Christmas we all had during Log4j 😉 ), the landscape hasn’t changed since 2024. The news feed is full of vulnerabilities, nation-state attacks, cybercrime activities, etc. I was reading a post by Ross Haleliuk on LinkedIn, which provided some good perspective. The cybersecurity world (and AI) is no exception; perfection is the enemy of good. You don’t need to be perfect in everything to be in a good position from a cyber perspective. Some companies even get away with, so to speak, murder from a cyber perspective with really bad practices, but they never seem to be significantly impacted... somehow. That’s something I want to keep in mind this year, as I think being able to take that step back is important!
2025 Cyber Predictions and Actions
I always dedicate some time during the holiday season to review the upcoming year’s cybersecurity predictions. This year was no different. I came across a comprehensive resource (part I and part II) listing predictions from 25 vendor reports about 2025. While these reports are vendor-centric and often influenced by their respective solutions, they still offer interesting insights.
Using AI tools, I consolidated the key takeaways into a threat-based perspective. This includes a heatmap, a focus on fundamental controls (which remain critical), and targeted initiatives linked to key threats.
Threat Prioritization
Below is a heatmap summarizing the likelihood, impact, and action urgency for key threats:
Threat | Likelihood | Impact | Action Priority |
|---|---|---|---|
AI-Driven Scams | High | Medium | Medium |
Autonomous AI Agents | Very High | High | Critical |
Evolved Ransomware | High | High | High |
Supply Chain Attacks | High | Very High | Critical |
Geopolitical Cyber Warfare | High | High | High |
Post-Quantum Threats | Medium | High | High |
This table is not an end but a beginning. True value lies in leveraging it to drive decisions, resource allocations, and response frameworks. That being said, the key is still in the basics!
Strengthening the Foundations: Core Controls for 2025
For any advanced strategy to succeed, robust foundational controls are essential. Here’s how to focus on the basics with measurable outcomes:
Asset Management: Transition from static inventories to dynamic, real-time visibility across IoT, AI, and cloud ecosystems.
Vulnerability Management: Move from ad hoc patching to SLA-driven processes automated by exploitation risk assessments.
Access Control: Adopt Zero Trust principles rigorously with phishing-resistant MFA and frequent privilege reviews.
Security Configuration: Implement “configuration as code” practices, ensuring automated drift detection and baseline enforcement.
Incident Response: Update playbooks for AI-specific threats, including deepfake and multi-agent attacks, supported by regular simulation training.
Bridging Risk to Action
Emerging risks demand more than recognition—they require structured responses that translate awareness into preparedness.
Autonomous AI Agents and Systems
The rise of autonomous AI agents introduces a new frontier of cybersecurity challenges, with increasingly complex attack vectors emerging from their independent decision-making and adaptive capabilities. These agents, when exploited, can infiltrate supply chains, persist undetected within networks, and even coordinate multi-agent attacks that evolve in response to defensive measures. Addressing these risks requires robust containment policies specifically designed for autonomous systems, ensuring that their operations are isolated and monitored to prevent lateral movement. In addition, organizations must implement governance frameworks that establish clear accountability for AI security while conducting rigorous and continuous security assessments of AI interactions and permissions. To further enhance defenses, AI-aware segmentation and behavior monitoring should be adopted, enabling the identification of anomalies and potential threats in real time. These measures collectively ensure that the adoption of autonomous AI does not outpace the resilience of the systems they integrate with.
AI-powered tools are transforming the threat landscape of social engineering, enabling the creation of highly sophisticated phishing campaigns, deepfake impersonations, and fraud attempts that challenge traditional identity protection mechanisms. These attacks leverage AI to craft convincing messages, voices, and visuals, often bypassing standard detection protocols. To combat this growing threat, organizations must deploy advanced anomaly detection tools capable of identifying unusual patterns in communication and behavior. Employee awareness and preparedness also play a crucial role; immersive training programs that simulate AI-generated manipulations can build resilience against these tactics. Furthermore, adopting phishing-resistant multi-factor authentication (MFA) methods and biometric solutions strengthens the integrity of identity verification processes, significantly reducing the likelihood of successful compromises.
Evolved Ransomware
Ransomware has entered a new phase of evolution, powered by automation and the early adoption of quantum-resistant cryptographic methods by threat actors. These developments make ransomware operations faster, more efficient, and harder to detect. To counteract this escalation, organizations need to integrate AI-powered intrusion detection systems capable of identifying and mitigating ransomware activities at their earliest stages. Transitioning encryption standards to quantum-ready protocols is another essential measure to stay ahead of attackers leveraging future-proof technologies. Additionally, robust backup and recovery mechanisms must be enhanced to ensure that organizations can quickly restore critical operations and data, minimizing downtime and disruption in the event of an attack.
Supply Chain Attacks
The reliance on open-source software and cloud dependencies continues to create significant vulnerabilities in supply chains, providing high-risk entry points for attackers. These risks are exacerbated by the complexity of modern supply chains, where a single compromised component can have far-reaching consequences. Mitigating these threats requires proactive supply chain risk assessments that identify and address weaknesses in vendor systems, software dependencies, and third-party integrations. Strengthened code review processes, combined with regular updates to open-source components, ensure that security is maintained at every layer of development. Collaborative risk management with vendors is equally crucial, fostering shared accountability and establishing stringent security standards to protect the integrity of interconnected systems.
What to work on in 2025?
Modernizing Security Foundations
A strong security foundation is essential for scalable and sustainable defenses, serving as the bedrock upon which advanced capabilities can be built. By addressing fundamental controls, organizations can significantly reduce their risk exposure while improving their overall security posture. This approach involves conducting comprehensive maturity assessments to identify gaps and prioritize areas for improvement. Automation of basic security controls—such as asset management, vulnerability patching, and access control—further enhances efficiency and reduces the burden on security teams. By modernizing these core capabilities, organizations create a robust platform for defending against both current and emerging threats.
Start Building AI-Powered SOCs
AI-powered Security Operations Centers (SOCs) hold the potential to transform threat detection and response by enabling real-time insights and automation. However, leveraging AI effectively requires a strong foundation of documentation and well-defined processes. Without this groundwork, AI tools lack the structured data and contextual understanding needed to deliver meaningful results. Organizations must first focus on documenting processes, workflows, and incident response procedures, including detailed runbooks and playbooks. Similarly, the data management layer—such as Security Information and Event Management (SIEM) systems—must be thoroughly mapped and maintained to ensure high-quality, actionable data is available for AI algorithms. By prioritizing the basics of documentation and process clarity, organizations set the stage for AI integration that enhances, rather than complicates, SOC operations.
Preparing for Quantum-Resilient Cryptography
The advent of quantum computing presents a looming challenge to current cryptographic standards, as it has the potential to render many encryption methods obsolete. Preparing for this disruption is critical to safeguarding long-term data security and ensuring compliance with emerging regulations. Organizations must begin by auditing their cryptographic assets to identify vulnerabilities and dependencies on legacy algorithms. From there, transitioning to quantum-ready protocols is a necessary next step to future-proof sensitive information. By acting now, organizations can position themselves ahead of the quantum curve, mitigating risks associated with delayed adoption and ensuring their cryptographic defenses remain robust for decades to come.
Beyond the Predictions
The cybersecurity challenges of 2025 demand more than awareness—they require action grounded in strong foundational practices and enhanced by forward-looking strategies. By modernizing security basics, integrating AI-powered operations, and preparing for the quantum era, organizations can address both current and emerging threats with confidence. The predictions may set the stage, but the true test lies in how effectively we turn these insights into actions that safeguard our systems and build a more secure digital future. The year ahead is an opportunity to not just react to threats but to proactively shape the defenses that will define the next decade of cybersecurity.
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Worth a full read
What Makes a “Good” Detection?
Key Takeaway
A comprehensive detection evaluation should integrate both qualitative assessments and quantitative metrics effectively.
Continuous adaptation to evolving threats is essential for maintaining the relevance and effectiveness of detection strategies.
The framework's flexibility allows organizations to prioritize metrics according to their unique security objectives and challenges.
Utilizing LLMs for evaluation can streamline the detection quality assessment process, enhancing operational efficiency.
Understanding the relationship between detection effectiveness and operational costs is crucial for resource allocation.
The alignment of detection strategies with organizational risk profiles ensures that security measures are relevant and targeted.
Actionable detections enhance the effectiveness of incident response efforts and improve overall cybersecurity posture.
A thorough understanding of threat landscapes enables organizations to better prioritize detection efforts and resources.
Effective communication among security teams can bridge the gaps caused by siloed functions and enhance detection quality.
The Shannon Signal Score framework represents an innovative approach to systematically evaluate and improve detection quality.
The (Anti-)EDR Compendium
Key Takeaway
EDRs monitor systems for abnormal behavior, requiring Red Teamers to use stealth tactics.
Knowing EDR architecture helps in designing effective evasion techniques during penetration tests.
EDR detection methods rely on correlation and analysis, making simple evasion tactics insufficient.
The effectiveness of EDR evasion often hinges on the execution flow of malicious code.
Continuous adaptation to evolving EDR technologies is essential for successful Red Team operations.
Memory encryption can significantly reduce detection rates during active scanning by EDRs.
Understanding both usermode and kernel-level telemetry is crucial for effective evasion strategies.
Bypassing EDRs demands a thorough understanding of how telemetry and event logging functions.
Crafting undetected shellcode loaders requires blending sophisticated techniques with practical execution.
The growth of EDR capabilities necessitates increasingly complex evasion strategies by attackers.
Research Paper
TheAgentCompany: Benchmarking LLM Agents on Consequential Real World Tasks
Summary: The paper introduces TheAgentCompany, a benchmark for evaluating AI agents' performance on real-world professional tasks in a simulated software company environment. It assesses AI agents' ability to autonomously complete tasks such as web browsing, coding, and communication, using both closed API-based and open-weight language models. The study finds that the best-performing agent can autonomously complete 24% of tasks, highlighting the potential and limitations of current AI systems in automating workplace tasks. The benchmark aims to provide insights into AI's impact on labor markets and guide future AI development.
Published: 2024-12-18T18:55:40Z
Authors: Frank F. Xu, Yufan Song, Boxuan Li, Yuxuan Tang, Kritanjali Jain, Mengxue Bao, Zora Z. Wang, Xuhui Zhou, Zhitong Guo, Murong Cao, Mingyang Yang, Hao Yang Lu, Amaad Martin, Zhe Su, Leander Maben, Raj Mehta, Wayne Chi, Lawrence Jang, Yiqing Xie, Shuyan Zhou, Graham Neubig
Organizations: Carnegie Mellon University, Independent, Duke University
Findings:
24% of tasks completed autonomously by best-performing agent.
AI agents struggle with complex, long-horizon tasks.
Benchmark simulates real-world software company environment.
Tasks include web browsing, coding, and communication.
Final Score: Grade: B+, Explanation: Strong empirical study with novel insights, but lacks detailed statistical analysis.
Wisdom of the week
The only limit to our realization of tomorrow will be our doubts of today
Contact
Let me know if you have any feedback or any topics you want me to cover. You can ping me on LinkedIn or on Twitter/X. I’ll do my best to reply promptly!
Thanks! see you next week! Simon