ISMS as prompt: write, deploy and maintain policies using AI
Leverage the power of LLMs, version control and automation to 10x your ISMS policy writing, deployment and maintenance
Information Security Management Systems (ISMS) provide a structured approach to managing risks and safeguarding sensitive information. They allow companies to comply with security standards like ISO 27001, which are essential for landing clients in regulated industries. With an ISMS, organisations demonstrate their commitment to information security and are able to build trust with clients.
ISMS challenges do not stop at the initial implementation. Unfortunately, they persist throughout the system's lifecycle. Organisations must continuously and rapidly adjust policies to align with the evolving technical landscape. The need to constantly adapt and rewrite policies is one of the most underestimated burdens placed on security teams.
Because of this, many security teams frequently pivot to professional Governance, Risk Management and Compliance (GRC) tools to manage the complexity of their ISMS. However, these tools are often expensive and demand significant time and effort to implement. While they offer robust solutions, the cost and resource requirements can be prohibitive for small teams with limited budgets.
Today, a more lightweight, flexible and cost-effective alternative is available to security teams thanks to AI. By leveraging the power of AI large language models (LLMs), GitHub version control, and GRC engineering principles, security teams can maintain their ISMS using a continuous development and deployment model similar to software development. This approach allows organisations to manage their ISMS sustainably and without incurring excessive costs.
In this post, we learn how to build an ISMS as prompt system, allowing security teams to write, deploy and maintain policies at unprecedented speed.
Solution overview
To drastically reduce policy drafting and deployment time, the simplest and most effective solution is to define ISMS policies as prompts and then use an AI LLM to construct and update the policies based on those prompts. For deployment purposes, the policies can be stored in a GitHub repository and then automatically uploaded to a Confluence wiki using a custom Python script and GitHub Actions.
LLMs today have reached a high-level of sophistication. However, if used without adequate direction and configuration, LLMs can still sometimes struggle to build functional ISMS policies. However, when paired with good prompting systems and safeguards, LLMs can generate high-quality and well-tailored policy drafts.
Additionally, a well-engineered ISMS as prompt system can be used to analyse existing policies, compare them with new prompt instructions and make precise updates only to the specific sections that require changes. Moreover, integrating LLMs with GitHub enables version control for policies, allowing security teams to track changes, approve updates and set up automated policy review cycles.
To build our ISMS as prompt system, we'll rely on LlamaIndex, a company that provides powerful tools to build LLM powered AI agents. More importantly, LlamaIndex grants the freedom to choose from a wide variety of LLMs, allowing security teams to tap into the best AI models for the job.
