Risk Assessment
Describe any AI use case and get a clear risk rating and a decision you can defend to your board and auditors, with the few things worth fixing first.
GenAI features and autonomous agents ship faster than anyone can review them, and traditional AppSec can't see the risk. Neo, our AI Control Architecture, wraps your AI in guardrails, governance and continuous monitoring, so you can prove it's safe and compliant without slowing releases.
Copilots, chatbots and agents are already in daily use. The proof that they're safe is not.
Copilots and agents are in daily use, but there's no documented answer the moment the board or an auditor asks.
Prompt injection, data leaking through retrieval context and unvetted third-party models slip past tools built for a different threat model.
Autonomous access with no boundary is one bad prompt away from cascading a bad decision across your systems.
Assessors now ask about model risk and data lineage, and producing that evidence becomes a six-week scramble.
Drift, data leakage and misuse go unnoticed because observability stops at the API gateway.
Usage rules are written down, but nothing stops an unreviewed model or agent going live.
Neo turns "we think it's fine" into a documented, defensible position, mapped to the tools you already run. No new stack to buy.
Describe any AI use case and get a clear risk rating and a decision you can defend to your board and auditors, with the few things worth fixing first.
Prompt-injection defense, output filtering and PII redaction applied at inference time, not after the fact, mapped to the tools you already own.
Model approval workflows, usage policy and acceptable-use enforcement, so nothing goes live unreviewed and the evidence is ready when anyone asks.
Scoped permissions and tool-call approval so an agent can't act beyond its remit, plus an inventory of every model and dependency you rely on.
One weak layer undoes the rest. We secure all four, from policy at the top down to runtime.
Model approval workflows, usage policy, and acceptable-use enforcement, so nothing goes live unreviewed.
Prompt-injection defense, output filtering, and PII redaction applied at inference time, not after the fact.
Scoped permissions, tool-call approval, and hard limits on what an agent can do autonomously.
LLM observability, anomaly detection, and full audit logging of every agent action.
General GRC tools were built for IT risk. Neo is built for AI specifically, covering the AI-native rules those tools don't, mapped into one control set.
The exact standards your customers, regulators and board already ask about.
AI management-system and risk-tier obligations, caught early rather than retrofitted.
Model-risk and cybersecurity rigor for regulated financial-services entities.
Existing compliance obligations mapped into the same control set.
Your board, auditors and insurer are already asking whether you can prove it's safe. Neo gives you a clear risk rating, the guardrails you actually need and evidence you can defend, in plain English.
Features go live before security ever sees them. We move the review into the pipeline so it stops being a bottleneck.
Zero-downtime DevSecOps delivered zero critical or high vulnerabilities reaching production post go-live, with every release infrastructure-as-code provisioned and gated by SAST, DAST, and SCA.
Autonomous access with no boundary is one bad prompt away from an incident. We scope it before that happens.
The question lands with no clear answer. We give you controls mapped to the standards they recognize.
Assessors now ask about model risk and data lineage. We make that evidence continuous, not a scramble.
A security data fabric processed 500M+ events daily in near real time across roughly 2,500 accounts on AWS, Azure, and GCP, on a cost-efficient OCSF lake with Bedrock GenAI for detection and incident summaries.
An embedded SOC, a dedicated pod of 12 analysts and a team lead, delivered near-24x7 coverage with no gaps across time zones, at a predictable cost and without the hiring overhead.
“Our board kept asking how we governed the AI features we were shipping, and we didn't have a clean answer. Flentas mapped our models and agent permissions against the OWASP LLM Top 10 and NIST AI RMF in weeks, not quarters. Every model in production now got there on purpose.”
CISOUS Cybersecurity Enterprise
One engagement is one stage. Here is what usually comes before and after, so the next step is always clear.
Build and run production agents with governance and cost under control.
Explore Agentic AI SolutionsGuardrails and evidence for the AI you are putting into production.
Security gates in every build and deploy, without slowing releases.
Explore Application Security & DevSecOpsMap controls once and stay audit-ready every day.
Explore Cloud Compliance & GovernanceA free AI Risk Assessment maps your models, prompt paths, agent permissions and control gaps, before an auditor or an attacker finds them first. No cost, no obligation.