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Two engagements. The same standard of evidence.

Sunshield tests networks, and the AI systems companies connect to their data. Both are scoped, authorized, and written for the people who have to act.

01 · Automated and manual

Network Penetration Testing

External and internal testing of the network you own. Scanners cover the ground. Manual testing decides which exposures are actually exploitable.

Request a Scope

A network penetration test at Sunshield is a scoped attempt to see how far an attacker could get, with evidence your team can verify. Automated tooling maps hosts, services, and common weaknesses. Manual work follows the paths that change the outcome: trust between systems, exposed management planes, and identity routes that a scanner lists but does not understand.

Automated coverage

Discovery and vulnerability identification across the in-scope ranges, so the engagement does not depend on someone remembering every host.

Manual testing

Hands-on validation of the issues that matter. We confirm impact, drop the noise, and follow access only inside the written scope.

What this is not

A raw scanner export. If a finding would not change a decision, it does not lead the report.

In a typical scope

  • External perimeter and internet-facing services
  • Internal networks when you provide access
  • Remote access paths that are part of the scope
  • Identity and trust relationships when they are in bounds

What you receive

  • An executive summary a non-specialist can read
  • Findings with severity, impact, and evidence
  • Remediation guidance an engineer can assign
  • A debrief, then one retest of the items you fix

Boundaries

  • Denial-of-service testing is out of scope unless a statement of work says otherwise
  • Social engineering and physical intrusion are separate engagements
  • We do not test systems you cannot authorize

What we need from you

  • Written authorization and a named point of contact
  • Asset lists, ranges, and explicit exclusions
  • A testing window and emergency contact
  • Credentials or access methods, if authenticated testing is in scope

02 · Security testing

AI Chatbot & Company AI Systems

Testing for chatbots, internal assistants, and retrieval systems that can see company data or take actions. The scope is the deployment, not a generic model.

Request a Scope

Companies are connecting assistants to tickets, files, and internal tools. Sunshield tests whether that system can be talked into revealing data it should hold, calling tools it should refuse, or treating untrusted content as instructions. We scope the model, the data it can reach, and the actions it can take.

The system, not the slogan

We test the assistant you deployed: its instructions, its data sources, its tools, and the accounts allowed to use it.

Data and actions

The questions are practical. Can a user reach another team’s data? Can untrusted content steer the assistant? Can a tool be invoked outside its intended use?

Evidence for the owners

Findings name the condition, the impact, and the change that closes it, written for the people who operate the system.

In a typical scope

  • Customer-facing and internal chatbots
  • Assistants connected to documents, tickets, or knowledge bases
  • Tool and plugin use, including actions the assistant can trigger
  • Access control between users, roles, and data sources

What you receive

  • A scope that names the system, data, and actions under test
  • Findings with evidence and business impact
  • Fixes ranked by what an attacker could actually reach
  • A debrief with the team that owns the assistant

Boundaries

  • We do not train or fine-tune models
  • We do not publish prompts, payloads, or exploit detail in the marketing site or in a public report
  • Production testing stays inside the hours and rate limits in the scope

What we need from you

  • A test environment, or written approval for production with limits
  • Roles that represent real users, including a low-privilege account
  • A description of connected data and tools
  • A contact who can explain intended behavior when we are unsure