Layer7Is your AWS account safe to run an AI agent in production?
Before you flip your agent to production, we review your AWS account and design for the failures that actually happen — account-wide blast radius, privilege escalation, and runaway spend with no hard-stop — then hand you a clear go-live risk call and a prioritized fix plan.
Teams ship agents faster than they secure them.
The expensive failures are predictable: a role that can invoke any model (account-wide blast radius), an agent that can escalate its own privileges, and no enforced stop on a runaway bill — AWS has no native billing kill-switch. Most incidents are not exotic exploits; they are coverage gaps in controls that looked fine.
We give you an independent, read-only read on exactly those risks — and the fixes — so you can go live with confidence instead of hope. It complements AWS-native guardrails; it does not replace them.
Areas we check
Best fit
Startups, SaaS teams, AI agencies, and teams adopting an agent framework who are deploying an AI agent to their own AWS account (Bedrock / AgentCore) and want a straight answer before go-live.
What you get from the review
A read-only scan, a senior review, a readable report, and a prioritized fix plan. No PDF theater.
Automated readiness scan
A read-only pass over your account — IAM blast radius, agent guardrail-bypass, spend hard-stop, and private network path — producing a scored readiness snapshot.
Senior manual review
The scan is the floor. A hands-on review with an OWASP-Agentic lens finds the coverage gaps a scanner cannot: escalation paths, fail-open checks, audit and data integrity.
Written findings report
Executive summary, genuine strengths, and prioritized findings — each with what we observed, its impact, a concrete fix, severity, and how to verify it.
Go-live risk call
A clear, defensible read: ready, tighten-first, or not-ready — and exactly why. Something you can take to your team, a customer, or an investor.
Prioritized remediation plan
What to fix, in what order, highest-leverage first — mapped directly to your findings.
Optional remediation
If you want the fixes done, Layer7 can implement the spend hard-stop, IAM scoping, private endpoint, and boundary hardening as a separate engagement.
The questions we answer before you go live
If one agent identity is compromised, can it reach your whole account — or only what it needs?
Can the agent escalate its own privileges or bypass its guardrails (for example via iam:PassRole or direct model calls)?
Is there an enforced, fail-closed stop on runaway spend that the agent cannot raise on itself?
Do your isolation and posture checks fail closed on the resource types nobody probed?
Is model traffic on a private path, and are secrets kept where they belong?
Are the highest-blast-radius actions run by deterministic code, or delegated to the agent?
Read-only. You never send us a key.
You choose how we get visibility, and everything is read-only and revocable. Run the scan yourself in your own AWS CloudShell and send back a findings file you can open first — or grant a revocable, least-privilege read-only role gated by an ExternalId. Report data is handled securely with a stated retention policy, and an NDA is available on request.
AI-agent security questions
Will you make my AI agent 100% secure or guarantee no incidents?
No. This is an honest, point-in-time review of specific, named risks — not a certification and not a guarantee. It finds the highest-leverage issues before go-live and gives you a clear plan to fix them.
Do I have to hand over my AWS keys?
Never. Everything is read-only, and you choose how. Run the scan yourself in your own AWS CloudShell and send us the findings file, or grant a revocable read-only role gated by an ExternalId. We never see a password or a secret key.
How is this different from Prowler, Security Hub, or AWS-native tooling?
Those are good and we complement them. This review frames the specific AI-agent deployment question — blast radius, excessive agency, runaway spend, guardrail bypass — and adds a senior human read plus a remediation plan, not just a scan.
Who is this for?
Startups, SaaS teams, AI agencies, and teams adopting an agent framework who are deploying an AI agent to their own AWS account (Bedrock / AgentCore) and want a straight answer before go-live.
How much does it cost?
Reviews start at $1,500 CAD for a focused engagement, with a fixed quote before any work starts. Done-for-you remediation is scoped separately.
What do I get at the end?
A written report with prioritized findings, a go-live risk call (ready / tighten / not-ready), and a remediation plan — plus a walkthrough call.
Request an AI-agent security review
Tell us about your AWS setup and your agent — or just your go-live timeline. We'll reply within one business day with the right scope and a fixed quote.
Milton, Ontario · Serving Canada & the US
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