Topic starter
11/06/2026 11:46 pm
TL;DR: Pillar Security says Frost & Sullivan named it the 2025 Competitive Strategy Leader in the global generative AI security market, citing asset discovery, adversarial red teaming, runtime guardrails, and enterprise deployment support across hybrid, cloud, and on-premise environments. The strategic shift is from point controls to lifecycle governance spanning discovery, testing, policy, and runtime enforcement.
Editorial analysis by NHI Mgmt Group, based on content published by Pillar Security: “A Milestone for Pillar: Honored as Frost & Sullivan's 2025 Competitive Strategy Leader for AI Security”.
By the numbers:
- Pillar Security says Frost & Sullivan named it the 2025 Competitive Strategy Leader in the global generative AI security industry.
Key questions
Q: How should teams govern AI assets and data together without creating duplicate controls?
A: Build a single governance inventory that links datasets, models, policies, and approvals, then reuse governance artefacts only when ownership, versioning, and review evidence remain intact.
Q: Why do static controls fall short for agentic AI security?
A: Static controls assume behaviour stays predictable after deployment.
Q: What are the signs that an AI security programme is missing runtime governance?
A: Common signs include incomplete asset inventory, testing that only checks malformed inputs, and guardrails that do not change when workflows, tools, or prompts change.
Practitioner guidance
- Map all AI assets across development and runtime Create an inventory that includes models, prompts, datasets, repositories, local environments, and connected identity providers so hidden assets do not remain outside governance.
- Test agentic systems with adversarial scenarios Use multi-step red teaming to validate how AI systems behave when prompts, tools, and workflows are combined in attack chains rather than in isolated tests.
- Bind runtime guardrails to live usage signals Feed production telemetry, red team findings, and threat intelligence into enforcement rules so policy adapts as tools and workflows change.
Bottom line: AI security is shifting from isolated controls to lifecycle governance that spans discovery, testing, runtime enforcement, and deployment boundaries.
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This topic was modified 1 day ago by NHI Mgmt Group
12/06/2026 8:35 am
AI security is becoming lifecycle governance, not a stack of point controls. The article’s strongest signal is that discovery, red teaming, runtime guardrails, and deployment architecture are being treated as one governance problem rather than separate product features. That shift matters because security teams cannot validate trust in AI systems if inventory, testing, and enforcement live in different operating models. Practitioners should manage AI security as an end-to-end lifecycle discipline.
A few things that frame the scale:
- 88% of organisations have embedded AI agents in their workflows, according to KPMG's 2026 report.
A question worth separating out:
Q: What is the difference between red teaming AI systems and ordinary security testing?
A: Ordinary security testing checks whether a control or input fails in isolation. Red teaming asks how an AI system behaves across a sequence of actions, including prompt manipulation, tool use, and workflow chaining. That distinction matters because many AI failures emerge only when the attacker can combine behaviours over time.
👉 Read our full editorial: Pillar named Frost & Sullivan leader for AI security strategy
This post was modified 1 day ago by NHI Mgmt Group