Harnessing AI for Corporate Cybersecurity: Insights from Rachel James at AbbVie
Cybersecurity is currently navigating a new arms race, where the use of artificial intelligence (AI) has become a critical weapon. AI serves a dual role, acting as both a formidable shield for defenders and a potent tool for malicious actors. With the complexity of the battlefield increasing, it is vital to possess a thorough understanding of both the technology involved and the intentions of potential attackers.
To gain insight into how these dynamics play out in practice, AI News spoke with Rachel James, Principal AI ML Threat Intelligence Engineer at AbbVie, a leading global biopharmaceutical company. She emphasizes the importance of leveraging AI to manage the overwhelming influx of security alerts.
“In addition to the built-in AI capabilities from our current tools, we also utilize large language model (LLM) analysis on our detections, observations, correlations, and associated rules,” James explains. Her team employs LLMs to identify patterns, eliminate duplicate alerts, and highlight vulnerabilities in their defenses before they can be exploited.
The team also relies on a specialized threat intelligence platform called OpenCTI, which allows them to synthesize diverse threat data into a cohesive overview. AI plays a crucial role in this process, helping to organize vast amounts of unstructured text into a standardized format known as STIX. James envisions a future where these language models will connect core intelligence with various aspects of their security operations, including vulnerability management and third-party risk analysis.
However, James is mindful of the inherent risks associated with using AI. She highlights her participation in the OWASP Top 10 for GenAI initiative, which focuses on understanding the vulnerabilities that generative AI can introduce. This awareness leads her to outline three key trade-offs that business leaders should consider:
- The unpredictable nature of generative AI and the acceptance of associated risks.
- The challenge of transparency in AI decision-making, which can become increasingly complex.
- The difficulty in accurately assessing the return on investment for AI initiatives, where overhyped expectations can lead to misjudgments on benefits and required efforts.
To fortify cybersecurity in the age of AI, understanding the mindset of attackers is paramount. With her background in cyber threat intelligence, James has conducted extensive research on how adversaries utilize AI technology. By tracking threats and tools through open-source channels and automated dark web collections, she shares her findings via her GitHub account, cybershujin.
As a co-author of the Guide to Red Teaming GenAI and the lead for the Prompt Injection entry for OWASP, James actively develops adversarial input techniques and collaborates with experts in the field.
Looking ahead, James believes there is a significant opportunity at the intersection of cybersecurity and data science. She points out that the cyber threat intelligence lifecycle closely resembles the data science lifecycle that underpins AI and machine learning. This alignment presents a unique chance for defenders to leverage intelligence data sharing alongside AI capabilities.
James encourages her peers in the cybersecurity landscape: “Data science and AI will be integral to every cybersecurity professional’s role going forward; embrace it.”
Rachel James will share her insights at the upcoming AI & Big Data Expo Europe in Amsterdam on September 24-25, 2025, discussing how to embed AI ethics in practice.
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