There’s an old military saying: “Slow is smooth, and smooth is fast.” In the current race to deploy AI, most companies are doing the exact opposite. Many AI teams are moving lightning fast without the right security in place. The result is speed up front and a grinding halt down the road when something goes wrong.
Organizations are pulling countless models from Hugging Face, conducting sporadic pen testing and building AI applications in a vacuum. They hope existing guardrails will catch issues before they shut down the business. But as any security professional knows, you can't secure what you don't know about.
When AI development is jerky — driven by "just trust me" promises rather than technical proof—you don't actually move faster. You just stop more abruptly when things break. At Palo Alto Networks, we believe Prisma® AIRSTM makes the AI lifecycle smooth, so your business can go fast.
The Silent Accumulation of AI Risk
AI risk rarely announces itself with a single, obvious failure. Instead, it accumulates quietly across the lifecycle in areas that often appear routine.
1. Risks in the Model Layer
A developer might pull a model from a trusted public repository. It passes basic validation, yet embedded within it is a load-time exploit. Because there is no external callback or obvious indicator, traditional security tools find nothing to flag.
2. Security Gaps During Testing
An LLM might pass a manual checklist or scripted tests, appearing production-ready. However, when evaluated using AI Red Teaming, it behaves differently, revealing sensitive data when prompted in specific ways that human testers rarely anticipate.
3. Runtime Threats
Prompt injection attacks often bypass firewalls because the traffic itself isn’t malicious at the network layer. The problem is semantic: the model is being coerced into producing unauthorized outputs, and traditional controls lack the context to recognize this in real time.
When a single model is compromised, it puts every downstream application at risk. The result is a full stop that pauses deployments and takes systems offline to audit the blast radius. Over time, this uncertainty erodes confidence for both engineering and security teams, creating friction that slows down innovation.
The Practitioner Visibility Gap
For most organizations, slowing down AI adoption isn't an option. Yet, saying "yes" to open source and third-party models without understanding what you are deploying means operating blind.
Whether they are downloading pretrained models into S3 buckets, fine-tuning open-source versions, or building internally, dev teams are rapidly ingesting and developing lots of different types of AI assets.
Meanwhile, security teams are often left blind to the critical context of these models. Traditional tools designed to look for bad IPs or malware signatures don't have the needed context for this new AI space. They cannot detect a neural backdoor or a license conflict until the application is already in production.
The Solution: A Test-Protect-Harden Loop
To scale securely, we must move beyond one-time manual assessments or point products that only secure one part of the lifecycle. Prisma AIRS is built to support the full test-protect-harden cycle. This approach creates a repeatable loop that allows teams to test risk, protect production, and then continuously harden systems.
1. Establish a Baseline with AI Red Teaming
We start by simulating attacker behavior against AI applications to uncover vulnerabilities before an adversary does. We establish a security baseline using an out-of-the-box attack library containing thousands of curated attack simulations from prompt injection to toxic content. This provides technical proof of exactly where a model's guardrails fail, such as identifying a successful jailbreak string that leaks internal system instructions.
2. Secure the Environment with Runtime Security
Once a vulnerability is identified, we don't leave it to chance. We move to runtime security to protect the application in production. By applying a security profile to the API key, the system acts as a real-time proxy, inspecting every incoming prompt and outgoing response. This allows the platform to recognize threats contextually and block them instantly.
3. Continue Hardening with Feedback Loops
Security isn't one and done. Attackers adapt, and so must your defenses. The goal isn't just to find vulnerabilities; it’s to create a feedback loop. We take intelligence gathered from production attempts and feed it back into the testing suite. We simulate sophisticated, context-aware attacks to further refine security logics with a Red Teaming Agent which operates as a contextual attacker that reasons and attempts to find ways around current runtime filters.
The Path Forward
Effective AI security doesn’t slow teams down. It removes the friction that inevitably arises when blind spots turn into breaches. To learn more about how Prisma AIRS is helping AI teams deploy AI securely, check out our latest webinar series, Deploy Bravely in the Age of AI.