The rush to adopt generative AI has created an explosion of new security tools targeting different aspects of an increasingly complex landscape. Every week, a solution promises to stop prompt injection, prevent data leakage, or harden models against attack. For security leaders, it can feel overwhelming. Which solutions matter, how do they fit together, and where are the gaps?
A Map for the Ecosystem
To bring clarity, OWASP released the LLM & GenAI Security Landscape Cheat Sheet. This community-driven resource condenses the broader framework into a practical reference that maps open source and commercial offerings against the full lifecycle of large language models and generative AI applications. The cheat sheet helps security leaders quickly see how different categories of tools align with real operational stages and where gaps may remain.
“The rapid evolution of AI technology has driven an explosion of solution approaches, which has only added to the confusion faced by organizations in determining where to allocate their security budgets.”
Why a Lifecycle Approach Matters
Unlike lists of risks or vulnerabilities, the landscape is structured around the lifecycle of AI applications. It begins with planning and data preparation, moves through development, testing, and release, and extends into deployment, operations, monitoring, and governance. Each stage highlights the security tasks that matter most. Testing calls for adversarial evaluation and benchmarking. Release requires supply chain validation. Operations depend on runtime self-protection, and governance anchors oversight and compliance.
“Traditional security tools may not be sufficient to fully address the complexities of AI applications, leading to gaps in protection that malicious actors can exploit.”
This lifecycle approach reflects the reality that AI risk is not a single problem with a single solution, but rather a chain of responsibilities that must be secured end-to-end, ideally by a single solution. It helps organizations benchmark their current coverage, identify blind spots, and compare vendor claims against a common standard. Without it, teams risk stitching together point tools that address only narrow slices of the problem while leaving major gaps exposed.
Bringing the LLM and GenAI App SecOps Framework to Life
The OWASP LLM and GenAI App SecOps Framework outlines security responsibilities across the full lifecycle, from development through operations and monitoring. Aqua Secure AI shows up across several of these critical stages, delivering visibility, assurance, and runtime protection without requiring code changes.
Develop and Experiment: Catching Risks in Code
In this stage, teams are integrating models into applications, writing prompts, and experimenting with logic. Aqua shifts security left by scanning source code to detect LLM use and flag unsafe practices like hardcoded prompts, unsafe outputs, or data exposures. It also validates how inputs and outputs are handled, helping teams prevent prompt injection and logic flaws before they ever reach production. By embedding these controls into the development workflow, Aqua ensures AI risks are discovered early and mapped directly to the OWASP Top 10 for LLMs.
Test and Evaluate: Securing AI Configurations
Before applications move forward, they need to be validated not just for accuracy but also for safe deployment. Aqua supports this stage with AI-SPM (AI Security Posture Management), which evaluates cloud AI services such as OpenAI and AWS Bedrock to ensure configurations are secure. It enforces assurance policies aligned to OWASP guidance, so teams can verify compliance and reduce risk before workloads are released.
Release: Protecting the Pipeline
At release, supply chain and delivery risks become critical. Aqua integrates into CI/CD pipelines to validate workloads, check container images for vulnerabilities, and enforce AI security policies automatically. This ensures that unsafe AI logic, risky dependencies, or misconfigured services are stopped before they reach production. By securing the release stage, Aqua strengthens supply chain trust and supports consistent DevSecOps practices.
Operate: Runtime Protection Where AI Lives
Once in production, AI workloads face dynamic and evolving threats. Aqua delivers real-time detection and response from inside the container where AI applications run. It stops prompt injection, jailbreaks, and model misuse as they happen, and it enforces runtime policies without requiring SDKs or code changes. Aqua also blocks post-compromise activity such as reverse shells or container drift, ensuring that even if attackers breach the application, they cannot compromise the environment underneath.
Monitor: Visibility Across the Enterprise
AI use does not stop at deployment. It evolves as teams adopt new services and models. Aqua provides comprehensive visibility into AI usage across SaaS platforms, managed AI services, and locally hosted models. It surfaces which models are running, how they behave, and whether usage aligns with policy, all mapped to the OWASP Top 10. This monitoring capability transforms shadow AI into measurable activity, providing organizations with a single source of truth for AI governance.
Unified Coverage Across the Lifecycle
Aqua provides coverage across Develop and Experiment, Test and Evaluate, Release, Operate, and Monitor, giving organizations protection in the most critical stages of the OWASP framework. Aqua’s coverage spans both AI application risks and the containerized workloads beneath them. This unified approach reduces blind spots, eliminates the overhead of stitching together point solutions, and gives security leaders confidence that their AI initiatives are secure end to end.
AI Application Security: Why This Matters Now
The OWASP GenAI Security Solutions Landscape represents a significant milestone for the industry. It provides a shared frame of reference in a noisy market and highlights the need for security that extends across every phase of AI adoption. For Aqua, it affirms an approach built on visibility, governance, and runtime security, delivered together in one platform.
If you are evaluating your AI security strategy, the OWASP Solutions Landscape Guide is a valuable starting point. Once you see how the lifecycle is mapped, you can quickly understand where your current tools provide coverage and where Aqua can help close the most challenging gaps.
