AI agents for cybersecurity firms: triage notes and questionnaires

Two different customers: MSSPs and product vendors

Managed security providers and consultancies spend analyst hours on repetitive triage: look up the IP, check the asset owner, read the last five related alerts, write the note. Product vendors spend sales-engineering and security-team hours on the other side: customer security questionnaires, trust-center requests and procurement reviews that ask the same 200 questions in 40 formats.

Both groups have an advantage over most industries: they understand the risk of automation. That makes scoping easier. It also means they ask harder questions about prompt injection, data handling and logging, which we welcome.

What we build

• An alert-enrichment agent that gathers context for a ticket, such as asset owner, recent related events, threat intel lookups and similar past tickets, and writes a triage note for the analyst.
• A questionnaire agent that matches each question to your policies, SOC 2 or ISO 27001 evidence and previous answers, drafts a reply with the source, and flags anything it cannot support.
• A client-report agent that assembles the monthly summary from ticket data, trends and notable incidents, for an account manager to edit.
• A vulnerability-intake agent that reads advisories, matches them to a client's asset list and drafts a prioritized list for an engineer.
• A knowledge agent that answers internal runbook questions with quoted steps and version dates.

Integrations may include a SIEM, ticketing tool and knowledge base, for example Splunk, Microsoft Sentinel, Jira, ServiceNow or Confluence. We connect through APIs with least-privilege tokens.

Where an agent helps and where it must not act

Questionnaire answers deserve special care. An overstated control in a customer reply can become a contract problem, so every drafted answer cites the policy or evidence behind it, and unsupported items are left blank.

The agent is part of your attack surface

An agent that reads tickets, emails or web pages can be manipulated by text hidden inside them, known as prompt injection. We treat all retrieved content as untrusted, separate instructions from data, restrict tools to the minimum, and require approval for anything with side effects. The OWASP project publishes guidance on risks for applications built on language models, and your team will know it.

We also protect client data: tenant separation, no cross-client memory, log retention rules and provider terms that exclude training on your inputs. Our AI agent security checklist and LLM evaluation checklist show the checks we run. If a customer or auditor asks how AI is used in your service, we help you document it.

How the project runs

1. Choose the repetitive taskUsually questionnaires for vendors and alert notes for MSSPs. We review about 40 past examples.
2. Build the evidence baseFor questionnaires we index your policies and past answers, tagging each with an owner and date.
3. Analyst pilotAnalysts rate each draft. Wrong or unsupported answers go into the test set.
4. Red-team itYour team tries to trick the agent with hostile content before it sees live data.

We work from Pune with offices in Coventry and New York, and book calls in your hours, including follow-the-sun handovers if you need them.

What it costs

Projects start at $1,500 and our rate is $30 an hour. A questionnaire agent over a curated policy set is a small project. Price rises with the number of tools, tenant separation needs and the testing your security team expects. You receive a fixed quote within one working day of a short call. See what an AI agent costs and AI agent development. For a client-facing assistant, an AI chatbot for IT companies covers that side of a services firm.