Generative AI is no longer a futuristic concept—it is here, transforming industries and redefining productivity. From crafting creative marketing campaigns to automating customer support, this technology, powered by large language models (LLMs) and neural networks, is reshaping how businesses work. But as organisations rush to adopt these capabilities, the question looms: how do we go about securing generative AI? This guide explores the security implications of generative AI, introduces a practical framework for assessing risks, and outlines actionable strategies for security leaders to protect their organisations while embracing this transformative technology.

Understanding Generative AI Security: The Foundations

Before going into security specifics, it is essential to grasp the fundamentals of generative AI. At its core, generative AI is just another data-driven computing workload. If your organisation has invested in robust cloud security practices—identity management, data protection, compliance frameworks—you are already ahead of the curve. However, generative AI introduces unique challenges that require nuanced approaches. For instance, if your application accesses sensitive databases or generates outputs using proprietary data, traditional security measures may not suffice. You will need to account for new risks, such as data leakage through model outputs or vulnerabilities like prompt injection attacks. This blend of old and new challenges underscores the need for a structured approach to securing generative AI workloads.

The Generative AI Security Scoping Matrix

To simplify this complexity, Amazon Web Services (AWS) has developed the Generative AI Security Scoping Matrix, which categorises workloads based on their level of ownership and control. This matrix helps organisations figure out their security responsibilities depending on how they use generative AI:

• Scope 1: Consumer Applications
  Using third-party apps like chatbots or generative tools with minimal customisation. Example: An employee uses a public chatbot to brainstorm ideas.
• Scope 2: Enterprise Applications
  Leveraging enterprise-grade tools with embedded generative features. Example: A scheduling app that drafts meeting agendas using generative AI.
• Scope 3: Pre-Trained Models
  Building custom applications by integrating pre-trained models via APIs. Example: Creating a customer support chatbot.
• Scope 4: Fine-Tuned Models
  Refining pre-trained models with proprietary data for specialised tasks. Example: Tailoring a foundation model to generate marketing materials.
• Scope 5: Self-Trained Models
  Developing entirely new models from scratch using proprietary datasets. Example: Training an industry-specific LLM for licensing purposes.

Each scope comes with distinct security considerations across five key disciplines: governance and compliance, legal and privacy requirements, risk management, controls, and resilience.

Key Security Disciplines

1. Governance and Compliance

For consumer (Scope 1) and enterprise (Scope 2) applications, scrutinise terms of service and ensure alignment with your organisation’s data governance policies. For higher scopes (3–5), where proprietary data is involved in training or fine-tuning models, governance becomes more complex. Policies must address data classification and usage restrictions to mitigate risks like unauthorised access or regulatory violations.

2. Legal and Privacy

Generative AI raises critical legal questions around data ownership and privacy compliance. For example:

• Does your model comply with GDPR’s “right to erasure” requirements?
• Are you prepared to retrain models if sensitive data must be removed?

For Scopes 4 and 5, where models are fine-tuned or self-trained with sensitive data, these concerns become paramount.

3. Risk Management

Generative AI introduces novel risks like prompt injection attacks, where malicious inputs manipulate model outputs. While these threats resemble traditional injection attacks (e.g., SQL injection), they need tailored mitigations such as robust input validation and threat modelling specific to LLMs.

4. Controls

Identity and access management (IAM) is still foundational but needs adaptation to generative AI workloads. Unlike databases that allow granular access controls, LLMs currently lack mechanisms to restrict access at the embedding level. Organisations must implement application-layer controls to enforce least privilege principles when interacting with models.

5. Resilience

Availability is critical for business continuity in generative AI applications. For higher scopes (3–5), ensure resilience through strategies like multi-region deployments, disaster recovery plans, and checkpointing during model training.

Prioritising Security in Practice

Once you have scoped your workload using the matrix, focus on immediate priorities:

• For Scopes 1–2: Strengthen governance by limiting sensitive data usage in consumer apps.
• For Scopes 3–5: Invest in robust threat modelling to address risks like prompt injection.
• Across all scopes: Collaborate closely with legal teams to navigate evolving regulatory landscapes.

Balancing Innovation with Responsibility

Generative AI offers unparalleled opportunities for innovation—but it also demands vigilance from security leaders. By using frameworks like the Generative AI Security Scoping Matrix and adapting existing cybersecurity practices to this new frontier, organisations can harness the power of generative AI without compromising on security or compliance. As you embark on your generative AI journey, remember that securing this technology is not just a technical challenge, it is a strategic imperative. The future belongs to those who can innovate responsibly while safeguarding trust.