CyberSolve

NeuroShield__CyberSolve
NeuroShield_CyberSolve
“NeuroShield exists because the identity stack enterprises spent two decades building (IGA, PAM, CIAM) was designed around humans and service accounts, and it cannot answer the most basic question AI agents now raise every minute in production: did the user do this, or did something act on their behalf? We built NeuroShield as a control plane that sits beside the action path rather than inside it, so customers adopt it without rewriting their applications or their identity stack. It is built on established industry standards and proven specifications, wiring open identity, delegation, policy, and audit together into one coherent flow that produces a signed answer to who the agent is, what it may do, who authorized it, how trustworthy it is right now, and what it did yesterday.This matters now because enterprise AI agents have moved from pilots into production faster than any technology cycle in the last decade, moving money, changing records, and accessing customer data at machine speed, while the governance layer around them has not caught up. The gap has already been named, from the EU AI Act’s high-risk obligations to frameworks like OWASP’s LLM Top 10 and MITRE ATLAS. The agents are already in production. The control plane is the part that is missing, and that is the gap NeuroShield bridges” 
– Diksha Bhatti, Senior Solution Architect (CyberSolve)

The Problem

AI agents are taking actions inside enterprise systems.

But very little of it is easy to use.
Teams often struggle to answer:

  • Approving invoices
  • Transferring funds
  • Creating users
  • Querying databases

Traditional IAM was built for humans. It was not built for agents.

 

When an AI agent acts, nobody can answer:

  • Who authorised this?
  • What was the scope of that authorisation?
  • Which policy governed it?
  • What exactly happened?

Teams rely on:

  • Prompt instructions
  • Code-level guardrails
  • Manual monitoring

What this leads to

  • Agents acting with full user token authority, far beyond what was intended
  • No record of what a user actually authorised an agent to do
  • Audit teams unable to trace what happened or why
  • No clean way to stop a misbehaving agent without disrupting users
  • Every enterprise AI deployment treated as an unmanaged identity risk

The Solution

NeuroShield is the IAM layer for AI agents.

It gives every agent:

  • A verified identity
  • A governed consent boundary
  • Enforced policies on every action
  • A live trust score
  • An immutable audit trail

Without changing any existing APIs, systems, or agent frameworks.

How it works

  1. Register the agent
    Give the agent a verified identity, an owner, and a defined set of allowed operations.
  2. Set policies
    Define what the agent can and cannot do using a visual policy builder. No coding required.
  3. Capture consent
    When a user authorises an agent to act on their behalf, NeuroShield creates a signed, scoped consent record with an expiry.
  4. Enforce on every action
    Every action is checked against policy before it executes. Allow, Deny, or Escalate.
  5. Audit everything
    Every decision, every action, and every revocation is logged and traceable to the user who authorised it.
  6. Suspend instantly
    Stop any agent platform-wide in under 5 seconds without touching the user's session.

What makes it useful

  • Minimal integration
    Three SDK calls added to the agent. Nothing changes in the systems it calls.
  • Policy in one place
    One UI for enterprise-wide rules and agent-specific restrictions.
  • Bounded authority
    Agents act within the scope the user approved, not with their full token.
  • Kill switch
    Stop any agent instantly, everywhere, without redeployment or disruption.
  • Continuous trust scoring
    Live score per agent. Drops trigger alerts before incidents happen.
  • Full audit chain
    Every action traceable back to the user, the consent, and the policy that governed it.

Where teams use NeuroShield

  • When AI agents act on behalf of customers in banking, insurance, or healthcare
  • When enterprise automation agents work inside ERP, HR, or finance systems
  • When a CISO needs to sign off on an AI deployment going to production
  • When regulators or auditors ask for evidence of AI agent controls
  • When multiple agents delegate tasks to each other in a pipeline
  • When an AI incident occurs and the full chain of events needs to be reconstructed

What you get

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