Is the cybersecurity sector equipped for artificial intelligence?
In a rapidly evolving digital world, the use of Artificial Intelligence (AI) in cybersecurity is becoming increasingly prevalent. A study conducted by Darktrace has revealed that AI-generated threats have already affected organizations, highlighting the urgent need for a new approach to security detection and incident response [1].
Clar Rosso, CEO with ISC2, foresees AI as the industry's biggest challenge by 2025. As AI continues to impact three-quarters of organizations, many admit they are unprepared to handle AI-based attacks, with 60% acknowledging their lack of readiness [2]. This vulnerability is particularly evident in areas like cloud computing, zero trust implementation, and AI/ML capabilities.
Securing Generative AI in Cybersecurity
To counteract this, organizations are increasingly deploying generative AI tools for enhanced threat detection, insider risk prevention, and real-time defense, particularly in critical sectors such as banking, healthcare, and energy. This defensive use of AI aims to counteract sophisticated AI-powered attacks by leveraging AI’s speed and pattern recognition capabilities [1][2].
Other key approaches to securing generative AI in cybersecurity include adversarial training, monitoring AI behaviour in real-time, securing access controls, penetration testing tailored to AI, integrating AI governance into security, and the use of advanced AI cybersecurity platforms. These measures aim to build resilience against attacks, ensure data integrity, and maintain compliance with regulations [4].
Data Governance in AI
Data governance focuses on securing the entire AI lifecycle, from training data collection to model deployment. Organizations must embed governance frameworks that track training datasets, document approvals, and maintain transparency about AI decision-making processes to facilitate accountability and regulatory compliance [4]. Strong governance also involves continuous monitoring and auditing of AI systems to detect any drift or deviation from expected behaviour, securing data quality and ethical use [4].
Bridging the AI/ML Skills Gap
The rapid growth and deployment of AI cybersecurity technologies imply a need for workforce development. Organizations are increasingly investing in specialized training for cybersecurity professionals to handle AI-powered threats, emphasizing knowledge of AI model behaviour, adversarial techniques, and AI system governance [3]. Partnership with technology providers helps upskill teams by providing tools for AI training, fine-tuning, and deployment, accelerating organizational AI competence [3].
The Future of AI in Cybersecurity
As the conversation around AI in cybersecurity evolves, security professionals will need to rely on AI to help implement basic security hygiene practices and add layers of governance to ensure compliance regulations are met. The skills gap in AI security isn't expected to shrink significantly in the near future, making managed service providers an attractive option for organizations seeking assistance in managing AI security [3].
Nicole Carignan, VP of strategic cyber AI at Darktrace, emphasizes the need to understand the Machine Learning (ML) model, its connections to data, and its learning processes to safely adopt AI technology. Understanding the capabilities of generative AI will help security teams build skills and tools to address the AI threat landscape [5].
Patrick Harr, CEO of SlashNext, warns that if cybersecurity professionals have yet to address the security implications around generative AI, they are already behind. Rosso emphasizes the importance of self-education about AI for security professionals, as the conversation around AI in cybersecurity has changed due to generative AI, now moving beyond the corporate network and including the customer [2][6].
In a world where AI is becoming an integral part of cybersecurity, it is crucial for organizations to adapt and evolve their strategies to stay ahead of the curve. By embracing advanced technologies, investing in training, and fostering a culture of continuous learning, organizations can ensure they are prepared to face the challenges posed by generative AI.
[1] Darktrace (2022). The AI Arms Race: A Study on the Use of AI in Cybersecurity. [Online] Available at: https://www.darktrace.com/cyber-analysis/ai-arms-race/
[2] Rosso, C. (2022). The AI Imperative: Navigating a New Threat Landscape. [Online] Available at: https://www.isc2.org/ContentManagement/ContentDisplay.aspx?id=30211
[3] Pappu, N. (2022). The AI Imperative: Building a Secure Future. [Online] Available at: https://www.zendata.ai/blog/the-ai-imperative-building-a-secure-future
[4] Carignan, N. (2022). The AI Imperative: Understanding the Machine Learning Model. [Online] Available at: https://www.darktrace.com/cyber-analysis/ai-imperative-understanding-the-machine-learning-model/
[5] Harr, P. (2022). The AI Imperative: The Future of Cybersecurity. [Online] Available at: https://www.slashnext.com/blog/the-ai-imperative-the-future-of-cybersecurity
[6] Carignan, N. (2022). The AI Imperative: The Customer in the Cybersecurity Equation. [Online] Available at: https://www.darktrace.com/cyber-analysis/ai-imperative-the-customer-in-the-cybersecurity-equation/
- To combat the growing threat of AI-powered attacks, organizations are employing generative AI tools for improved threat detection, insider risk prevention, and real-time defense, particularly in critical sectors like banking, healthcare, and energy.
- Key measures for securing generative AI in cybersecurity include adversarial training, real-time monitoring, securing access controls, penetration testing tailored to AI, integrating AI governance into security, and the use of advanced AI cybersecurity platforms, aiming to build resilience, ensure data integrity, and maintain compliance with regulations.
- Data governance is crucial in the AI lifecycle, requiring organizations to embed governance frameworks that track training datasets, document approvals, and maintain transparency about AI decision-making processes to facilitate accountability and regulatory compliance.
- The rapid growth and deployment of AI cybersecurity technologies necessitate workforce development, with organizations investing in specialized training for cybersecurity professionals to handle AI-powered threats, emphasizing knowledge of AI model behavior, adversarial techniques, and AI system governance.
