From writing code to serving customers, companies worldwide are capitalizing on AI to enhance their applications and business processes. But this boom is poised to turn into a bust if security can’t keep pace with AI adoption. And make no mistake: LLMs and AI services, whether they are consumed by the application or hosted and exposed via APIs, are increasingly at risk.
Attack surfaces are constantly expanding, and they’re outpacing controls as “do it now” AI mandates supersede “do it right” security best practices.
Just as CSPM gave organizations much-needed visibility into cloud misconfigurations, AI-SPM has emerged to give security teams a baseline understanding of how AI is being used across the business. But visibility, while important, is only the first step.
Visibility is the First Step, Not the Finish Line
Many security teams are still developing their approach to AI security, keeping close tabs on evolving business use cases and maturing security benchmarks and industry standards. This has led to AI-SPM becoming the first step in AI security strategies.
AI-SPM gives organizations a map of their AI usage: who is using which models, where the data is flowing, and whether policies are being followed. This is essential, but visibility alone does not prevent an active exploit. Knowing you have shadow AI or a misconfigured policy does not stop an attacker from abusing it in real time.
This is the same limitation we saw with CSPM in the begining of the cloud era. CSPM could identify risky configurations, but it could not stop an attacker who was already inside. Container security requires runtime protection to close that gap. AI workloads now face the same reality.
Where AI-SPM Fits in an AI Security Strategy
AI-SPM helps organizations answer key questions: Which AI models and services are being used? How are they integrated? Where are the misconfigurations?
With this baseline, security teams can reduce their attack surface, uncover shadow AI, and begin to enforce governance.
But it cannot be the last step, especially with the new threat landscape of AI. Prompt injections, model inversion, and leaky outputs are all exploited at runtime, which is right where conventional AI-SPM solutions are blind. Unsurprisingly, these blind spots have contributed to a 400% surge in runtime attacks as AI adoption has skyrocketed. These risks can’t be fully understood or mitigated by static checks or logs. They only become visible when the AI system is running and interacting with users, tools, or data.
Attackers are adaptive, constantly experimenting with new prompts, exploiting context windows, and chaining together integrations in creative ways. Without runtime defenses, organizations are relying on static guardrails that adversaries can bypass in minutes.
Visibility and Real Time Protection: Strong in Isolation, Stronger in Unison
AI-SPM provides governance and oversight, but to close the loop and create a holistic AI security strategy, companies need runtime protection. This combination of preventative scanning and proactive enforcement is key to delivering end-to-end AI application security, from prompt to production.
With runtime protection, security teams can:
- Monitor AI workloads in real time, extending visibility beyond static reports
- Detect and prevent suspicious activity without code changes, blocking attacks like prompt injection or jailbreaks
- Enforce governance policies during live execution to stop sensitive data leakage and insecure access attempts
This dual approach mirrors the cloud journey: posture management gave visibility, but workload protection delivered resilience. AI security requires the same approach.
Aqua Secure AI: From Cloud to Code to Prompt
From the outset, Aqua has always believed that a mix of preventative scanning and proactive runtime defense is key to delivering true end-to-end security for cloud native applications.
Designed from the ground up with a breadth of capabilities that complement each other, Aqua Secure AI provides full lifecycle security for AI applications. Unlike other solutions that only cover one part of the security equation, Aqua Secure AI is a complete security command center that gives security teams:
- Accurate code scanning: Comprehensively scan your code for unsafe AI usage during development, streamlining security and risk management across the software development lifecycle.
- Customizable security guardrails: Enforce AI security best practices during development without friction with preventative security gates, all mapped to OWASP’s Top 10 Threats for LLMs.
- Proactive runtime defense: Identify unsafe AI usage, detect suspicious activity and stop malicious activity, from prompt injections to jailbreak attempts, without requiring any code changes.
- Organization-wide visibility: Get a better understanding of AI-related risks with comprehensive visibility into the AI models, platforms, and versions used across environments, all in one place.
AI is redefining how applications are built, and that trend shows no sign of slowing. IDC predicts more than 1 billion new AI applications will be live by 2028, which translates to more than 10 billion containers deployed across cloud native environments that could be targeted!
Now is the time to start acting to secure your AI applications.
Customers are already using Aqua Secure AI to get runtime visibility into AI workloads, detect unauthorized AI models, apply OWASP-approved assurance policies, and more.
Join our exclusive early access program and see what Aqua Secure AI can do for you!
