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12/06/2026 12:13 am
TL;DR: Generative AI changes application security from a pre-deployment gate into a continuous runtime discipline, because prompt injection, data leakage, jailbreaks, and fast prompt iteration emerge only in live use, according to Pillar Security. Static QA and benchmark-only controls no longer cover the real failure modes of AI systems.
NHIMG editorial — based on content published by Pillar Security: From Static Scanning to Recursive Loops: Lessons from a Decade in Data Science and AI
Questions worth separating out
Q: How should security teams govern AI systems that change through live prompts?
A: Security teams should treat prompts as governed change objects, not informal text.
Q: Why do static tests miss the real risks in generative AI applications?
A: Static tests miss the core risks because many failures only appear when the system is running with live users, live data, and adversarial inputs.
Q: What do security teams get wrong about deploying AI safely?
A: They often assume deployment marks the end of assurance, when it actually marks the beginning of continuous governance.
Practitioner guidance
- Move security validation into runtime Add continuous checks for prompt injection, jailbreak attempts, and sensitive-data exposure after deployment, not just before release.
- Version and approve prompts like production code Track prompt authorship, changes, and rollback paths in the same governance process you use for application releases.
- Bind identity controls to model and data access Review which service accounts, users, and applications can supply context, retrieve data, or trigger model actions.
What's in the full article
Pillar Security's full blog post covers the operational detail this post intentionally leaves for the source:
- Step-by-step examples of how prompt changes alter model behaviour in production.
- Specific examples of runtime failures such as prompt injection, data leakage, and jailbreaks.
- The article's full discussion of why post-deployment monitoring must replace the old release-only security mindset.
- The author's first-hand perspective on building and securing LLM-native systems over time.
👉 Read Pillar Security's analysis of why static scanning fails for LLM-native applications →
Agentic AI runtime governance: what static scanning misses?
Explore further
12/06/2026 9:19 am
Static security controls were built for software that stays still, but LLM-native systems keep changing at runtime. This article shows why pre-release testing is no longer the centre of gravity when prompts, retrieval, and user input can reshape behaviour after deployment. The implication is that AI governance now has to assume live variability as the normal condition, not the exception.
A few things that frame the scale:
- 98% of companies plan to deploy even more AI agents within the next 12 months, despite documented rogue behaviour in 80% of current deployments, according to AI Agents: The New Attack Surface report.
- Only 52% of companies can track and audit the data their AI agents access, leaving 48% with a complete blind spot for compliance and breach investigation.
A question worth separating out:
Q: How can organisations reduce prompt-injection risk in production AI systems?
A: Organisations reduce prompt-injection risk by limiting what the model can see, validating untrusted input, and monitoring output for policy violations. They also need strict control over any tools or data sources the model can reach, because the attack becomes more dangerous when the model can act on manipulated context.
👉 Read our full editorial: Agentic AI changes security from static scans to live governance
