The AI Security Playbook: Match Your Testing to Your Maturity
In customer conversations, a recurring theme emerges: Artificial Intelligence (AI) is advancing faster than regulations can keep pace. Organizations that fail to test comprehensively don’t just face technical issues; they risk damaging their brand, incurring regulatory penalties, and losing customer trust.
Three forces are converging to make this urgent:
- Regulators are tightening expectations on transparency, fairness, and robustness.
- Attackers are exploiting vulnerabilities like prompt injection and data leakage.
- Customers demand not just functionality, but also responsible and secure AI.
Our fieldwork shows that teams treating AI security as a one-off check quickly fall behind. The leaders approach it as a journey of maturity, starting with safety, expanding into security, and ultimately building trust. That journey requires adversarial testing tailored to each stage of the development process.
Security, Safety, and Trust: The New Triad
Most AI incidents don’t begin as deliberate attacks. They start with unintended behavior: an assistant oversharing sensitive data, a chatbot hallucinating facts, or an agent with excessive permissions. That’s why securing AI requires clarity on two distinct but intertwined surfaces:
- AI Security: Involves preventing unauthorized access to AI systems and safeguarding them from potential vulnerabilities. This includes protecting against information leakage about other users and maintaining the integrity and confidentiality of the AI infrastructure.
- AI Safety: Focuses on preventing AI systems from generating harmful content, such as providing instructions on creating bombs or producing offensive language. It aims to ensure responsible use of AI and adherence to ethical standards.
Together, security and safety form the foundation of AI Trust, the confidence that systems behave as intended, align with your policies, and remain resilient against real-world abuse.
Shared Responsibility Still Applies
Using a major provider’s foundation model doesn’t absolve you of risk. Like cloud, AI follows a shared responsibility model: the provider secures the base model and service, while you are accountable for how it’s used in your environment; prompts, integrations, code, and data paths make up your unique attack surface. Most organizations consume AI as Software as a Service (SaaS), and many build features using Platform as a Service (PaaS) APIs; your real-world risk is driven by your side of the stack: configuration, data governance, and safe usage.
In PaaS (common for teams building with hosted models):
- Provider: Hosts and hardens the foundation model; secures platform and training-data handling; offers baseline guardrails/filters and service telemetry.
- You: Secure implementation and usage; system prompts and grounding/Retrieval-Augmented Generation (RAG) data, plugins/tools/agents and their permissions, tenant/user access controls, data residency/retention, logging/monitoring, and incident response.
In SaaS (AI embedded in your apps):
- Provider: Operates and secures the model and SaaS platform, enforces cross-tenant isolation, provides service-level guardrails and connector frameworks, and maintains availability and update hygiene.
- You: Secure tenant configuration and data governance: scope connectors and least-privilege access, control which data the copilot can see, enforce Data Loss Prevention (DLP), retention, eDiscovery, manage Role-Based Access Control (RBAC) and acceptable-use policies, enable auditing/monitoring, and routinely test for data leakage or unsafe outputs via realistic user behaviors.
What’s Old Is New Again
The good news is that AI security isn’t an entirely new discipline, as many of the top risks are variations on well-known security flaws. That means your existing AppSec expertise is directly transferable, provided you adapt it with the right testing strategy.
For example, the following AI-specific vulnerabilities are just new variants of existing vulnerabilities:
- Prompt Injection is the new SQL Injection.
- Excessive Agency is the new Privilege Escalation.
- Sensitive Information Disclosure is the new Data Leakage.
The AI Security Maturity Journey
Every organization that adopts AI moves through predictable stages of maturity, and AI risk evolves as its systems mature. Knowing where you are helps you focus your testing on the risks that matter most.
Use the AI Security Maturity & Readiness Checklist to assess where your organization stands, identify blind spots, and map the right testing strategy to your stage.
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