# Adapting cybersecurity frameworks to manage frontier AI risks: A defense-in-depth approach

arXiv: https://arxiv.org/abs/2408.07933 · HTML: https://mlsanity.com/paper/2408.07933/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Aug 2024.

## Built on by (most cited first, 4 of 4)

- [Distributional AGI Safety](https://mlsanity.com/paper/2512.16856/) (2025)
- [Neural Chameleons: Language Models Can Learn to Hide Their Thoughts from Unseen Activation Monitors](https://mlsanity.com/paper/2512.11949/) (2025)
- [AI Governance through Markets](https://mlsanity.com/paper/2501.17755/) (2025)
- [Organizational Adaptation to Generative AI in Cybersecurity](https://mlsanity.com/paper/2506.12060/) (2025)

## Abstract (arXiv)

The complex and evolving threat landscape of frontier AI development requires a multi-layered approach to risk management ("defense-in-depth"). By reviewing cybersecurity and AI frameworks, we outline three approaches that can help identify gaps in the management of AI-related risks. First, a functional approach identifies essential categories of activities ("functions") that a risk management approach should cover, as in the NIST Cybersecurity Framework (CSF) and AI Risk Management Framework (AI RMF). Second, a lifecycle approach instead assigns safety and security activities across the model development lifecycle, as in DevSecOps and the OECD AI lifecycle framework. Third, a threat-based approach identifies tactics, techniques, and procedures (TTPs) used by malicious actors, as in the MITRE ATT&CK and MITRE ATLAS databases. We recommend that frontier AI developers and policymakers begin by adopting the functional approach, given the existence of the NIST AI RMF and other supplementary guides, but also establish a detailed frontier AI lifecycle model and threat-based TTP databases for future use.