SecuredAI is a boutique security firm built for one problem: customer chatbots, internal RAG, and agentic AI workflows fail in different ways — and defending them usually means stitching together red teams, guardrail vendors, and IAM consultants who don't talk to each other. We run all three under one team, one methodology, one point of contact.
Agent & LLM security. F around and find out.
A prompt-injection scanner built for a chatbot won't catch a RAG poisoning path, and neither will catch an over-permissioned agent calling a production API. We treat each surface as its own discipline, with its own attack library and its own control set.
Every public channel is an open input to a model that can see account data, pricing logic, and internal instructions. Attackers don't need a login — just a conversation.
RAG collapses your access-control model into a single answer box. If retrieval doesn't respect document-level permissions, the model will happily quote the one file an employee shouldn't see.
Once a model can call tools, a manipulated conversation becomes a manipulated action — a refund issued, a ticket escalated, a query run against production. This is identity and access management with a nondeterministic actor in the loop.
Most organizations end up assembling this matrix themselves — a red team firm here, a guardrail vendor there, an IAM consultancy for the rest — none of whom share findings. We run all nine cells as a single engagement with a shared risk register.
Multi-turn jailbreak & injection campaigns across web, mobile, and voice, scored against your policy, not a generic benchmark.
Policy-aware input/output filtering and gateway rules calibrated from your red team findings, not off-the-shelf defaults.
Ensures the bot only ever answers with what the authenticated user is entitled to see, across every channel.
Adversarial documents and indirect injection payloads planted to test whether retrieval leaks across permission boundaries.
Query and chunk-level filtering that enforces document ACLs at retrieval, not just at the front door.
Reconciles index permissions against the underlying document store so retrieval can never outrun access rights.
Simulated goal-hijacking and privilege-escalation paths across your APIs, ticketing systems, and MCP integrations.
A control point in front of every tool call — approving, rewriting, or blocking actions before they hit production systems.
Scoped, short-lived credentials per agent and task, with full action-to-intent audit trails for every tool invocation.
The sequence matters: we don't harden a surface before we understand how it actually breaks, and we don't govern access before defenses are calibrated against real findings.
Map every chatbot channel, RAG pipeline, and agentic workflow in scope — including the tools, APIs, and MCP servers each agent can reach. Most surprises live here.
Red team each surface with attack techniques specific to it: conversational jailbreaks, retrieval poisoning, tool-chain hijacking. Every finding is reproducible and severity-scored.
Deploy or tune runtime guardrails and gateways calibrated directly from the findings above — not generic policy packs.
Rebuild identity and access boundaries around what the testing actually revealed: least-privilege scopes for agents, permission-aware retrieval, entitlement-aware chat responses.
Stand up ongoing detection and a shared risk register across all three surfaces, so new features and new agents are assessed before they ship, not after an incident.
Findings and controls are mapped back to frameworks your security, risk, and audit teams already recognize — so results are portable into existing governance, not a parallel process.
Answer a few questions about your chatbot, RAG, and agentic footprint and get a risk profile with concrete next steps, mapped to the matrix above.
Every engagement starts with a short scoping conversation — no obligation, no generic sales deck.