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Sharkzone

SOLUTIONS & AI AUTOMATION

AI agents that do real security work.

We implement proven solutions inside your company and build new automation from scratch, using AI where it adds value and keeping people in the loop where it matters.

PLUG-IN SOLUTIONS

Pre-built. Plug into your environment.

Security solutions built as agents that plug into your repositories, pipelines and AI platforms. Available solutions are already running in production.

AvailableAI AppSec Agent

Secure Code Review

Secure Code Review puts an AI AppSec agent in your pull request workflow. It analyzes each change for security issues in context, clears low-risk changes automatically and routes findings, errors and uncertain cases to human review, where false positives are confirmed or dismissed. Sharkzone implements it in your repositories and CI and tunes it to your stack and risk policy.

  • Every pull request reviewed for security
  • Most changes cleared without waiting for a human
  • AppSec time spent on the cases that need it
Talk about Secure Code Review
Production average
of pull requests cleared by the agent
~94%
needed human intervention
~6%

Average based on more than 2,000 pull requests reviewed in production. Human review covered errors, vulnerabilities and uncertain cases, including false-positive checks. Rates depend on codebase and risk policy.

Scope

  • AI review of every pull request
  • AppSec analysis in code context
  • Authentication and access control
  • Business-logic flaws
  • Injection and data-handling issues
  • Secrets and sensitive-data exposure
  • Dependency and supply-chain signals
  • Automatic clearance of low-risk changes
  • Escalation to human review
  • False-positive triage

How it is delivered

  1. Implementation in your pipeline

    The agent is deployed in your repositories and CI, tuned to your stack, security policies and risk thresholds.

  2. Human fallback by design

    Escalation rules, a review queue for your team and a false-positive feedback loop that keeps the agent improving.

  3. Measurement

    Automation rate, escalation rate and findings tracked over time, so the agent is tuned from evidence.

How it works

  1. Pull request opened

    Every change triggers a review automatically, with no extra step for developers.

  2. Agent reviews like AppSec

    The agent reads the change in context and checks it against your security policy.

  3. Cleared or flagged

    Low-risk changes are cleared. Findings are explained in the pull request.

  4. Escalated when unsure

    Errors, vulnerabilities and uncertain cases go to your team for a human decision.

IN DEVELOPMENT

Next solutions on the way. Talk to us if you want to shape them with your environment.

  • In DevelopmentAI Threat Modeling

    Threat Modeling Agent

    An agent that builds and maintains threat models from your architecture, code and documentation, and keeps them current as the system changes.

    • Architecture and data-flow mapping
    • Trust boundaries and entry points
    • Threats by component
    • Prioritized mitigations
    • Updates as the system changes
    • Human review of the final model
    Ask about Threat Modeling Agent
  • In DevelopmentAI Agent Security

    Agent, MCP & Skills Review

    Security review for AI agents, MCP servers and skills before they reach your environment: permissions, tool-abuse paths, prompt-injection exposure and data access.

    • MCP server and tool review
    • Agent skills and instructions
    • Excessive permissions and scopes
    • Prompt-injection exposure
    • Data access and exfiltration paths
    • Supply-chain and provenance
    Ask about Agent, MCP & Skills Review

BUILT FROM SCRATCH

AI automation & agents, built for your workflows

When no ready-made solution fits, we design and build automation from scratch: AI agents where judgment at scale matters, plain automation where it is simpler and more reliable.

What we build

  • AI agents in production workflows
  • Security triage and finding enrichment
  • Alert and ticket automation
  • Security data processing
  • DevSecOps and CI/CD integrations
  • LLM integrations with guardrails
  • Internal tools and workflow automation
  • Engineering workflows beyond security
Talk about an automation

How we build

  1. Map the workflow

    Find where volume, waiting time and repetitive judgment cost the most.

  2. Use AI where it earns its place

    AI where it adds measurable value; deterministic automation where it does not.

  3. Design the human fallback

    Every agent knows when to escalate. People handle exceptions, errors and uncertain cases.

  4. Measure in production

    Track automation rate, escalations and false positives, and tune from evidence.

AI agents are also an attack surface. Everything we build goes through the same AI security practice we offer clients.

WHERE TO START

Start where you need it most.

Some engagements start with a security test, others with a workflow that is costing your team too much time. Tell us where you are and we will suggest the right starting point.

Get in touchExplore services