Comparing AI security controls with the controls your cloud, identity and application platforms already provide
Compare on one question: does the AI security control add a capability you genuinely lack, or does it duplicate one you already own?
Direct answer
Compare on one question: does the AI security control add a capability you genuinely lack, or does it duplicate one you already own? That depends on your existing stack, not on the vendor's feature list - so the honest way to run the comparison is to check, domain by domain, whether your current controls can express the policy the AI scenario actually needs.
A practical comparison framework
| Domain | What platform controls typically cover | The check to run for AI agents |
|---|---|---|
| Identity and access | Authentication, role-based permissions, privileged access management | An agent acting within legitimate permissions can still be manipulated into abusing them. Can your identity controls evaluate whether this specific action, right now, is appropriate - or only whether the identity is allowed in principle? |
| Data protection | DLP, encryption, classification | Agents assemble context dynamically. Can your data controls see what reaches the model through prompts, retrieved documents and tool outputs - not just through known, rule-based flows? |
| Network and API | Firewalls, API gateways, segmentation | Agents choose tools and arguments at runtime. Can your network controls express policy over a call graph that is not fixed in advance? |
| Application security | Code scanning, WAF, dependency checks | Natural-language instructions - in prompts, documents, web pages and tool results - become an attack surface. Do your existing tools inspect that layer? |
How to use the table: if your existing controls already answer a row's check for your environment, your incremental need there is smaller - some modern platforms do offer contextual, behavior-aware policies, and where yours do, an AI security control should integrate rather than duplicate. The rows where you get no answer are where an AI-specific control has to earn its budget: typically the behavior layer, where content, context and intent are evaluated before an action executes.
Governance note
NIST's AI Risk Management Framework is structured around distinct, complementary functions (GOVERN, MAP, MEASURE, MANAGE - airc.nist.gov/airmf-resources/airmf/5-sec-core). Whatever you add, mapping it into your existing review and incident processes from day one is easier than retrofitting later.
Where Fencio fits
Fencio is an AI firewall that checks what an agent is about to do before it acts - the behavior layer in the table above. It evaluates content, context and intent, and enforces policy outside the model on supported integration paths.
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