Agentic AI Security
Runtime controls and signed records for agents that act.
Glacis helps fast-growing AI companies supervise agents that use tools, credentials, customer data, and delegated authority — then give enterprise buyers signed records of covered control decisions they can verify offline.
The risk
Agents are no longer chat windows. They are delegated systems.
They can retrieve data, call APIs, trigger workflows, write files, update tickets, and act inside customer environments. That makes prompt injection, tool misuse, exfiltration, and unauthorized action business risks, not abstract AI safety concerns.
Enterprise buyers will ask what the agent could do, what it was blocked from doing, and how you know. Glacis connects configured runtime controls to signed operational records for covered actions.
Founder-led sales
Get one agent workflow through enterprise security review.
When an agent uses tools, credentials, customer data, code, or production systems, enterprise buyers ask for proof. Glacis helps technical founders harden one named workflow and produce a customer-ready evidence pack before the security review stalls the deal.
Security-review pressure
Answer how prompt injection, tool misuse, data leakage, unauthorized actions, and drift are controlled.
No mature security team yet
Give fast-growing teams runtime security depth before the organization has a full security function.
Expanding attack surface
AI-assisted development and agent workflows increase access to tools, code, credentials, and production data.
One-workflow Sprint
Map one named workflow, prioritize runtime controls, and leave with an evidence pack customers can review.
Control surface
Control what agents can do at decision time.
Tool permission
Allow, block, or escalate sensitive tool calls before the agent acts.
Credential boundary
Limit which credentials, scopes, and systems the agent can reach, and record the configured boundary.
Data access
Redact, block, or escalate risky data movement and suspicious context use.
Human review
Require approval when impact, confidence, or policy context crosses a threshold.
Operational evidence
Show what each agent was allowed to do, blocked from doing, and why.
Receipts are generated at runtime. Evidence packs are assembled from receipts.
That gives security reviewers a concrete artifact instead of a policy promise.
Assurance workflow
Use one agent workflow to make supervision independently checkable.
Map delegated authority
Identify credentials, tools, data, workflows, and actions the agent can reach.
Install runtime controls
Set allow, block, redact, escalate, and review rules at the agent boundary.
Preserve signed operational records
Produce signed records of reported outcomes for covered actions. Review packets are scoped and assembled separately for a particular engagement.
Architecture
Add operational evidence to the stack you already use.
Glacis complements observability, governance, and security tools with signed records of what configured runtime controls reported. A local, data-minimizing path can exclude selected prompts, outputs, customer data, credentials, and context from the portable record; verify surrounding model, telemetry, and storage flows separately.
Runtime controls
Control the agent boundary before tools and data are touched.
Signed evidence
Preserve signed operational records of reported outcomes for covered agent actions.
A bounded evidence path
Keep protected payloads local in a configured deployment while exporting the hashes, outcomes, signatures, and metadata a reviewer needs.
Bring one agent workflow.
We’ll show where delegated authority creates risk, which controls should run, and which signed operational records could support a scoped customer review.
Discuss a consequential AI workflow