AI Application Security

Find vulnerabilities. Build policies. Protect agent actions.

Discover AI functionality in your code, test agent workflows, generate policies from findings, and enforce controls across connected model and tool traffic.

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The Dwaar approach

Application discovery
Find AI integrations and endpoints
Red teaming
Test agent workflows
Policy generation
Turn findings into controls
Runtime control
Evaluate connected traffic and actions
Evidence
Investigate policy violations

The challenge

Every AI feature creates a new security boundary.

Unidentified AI functionality

Model integrations, tools, and agent endpoints can spread through a codebase without a shared view of the attack surface.

Exploitable workflows

Prompt injection and tool misuse can turn a legitimate agent workflow into a path to sensitive data.

Unsafe runtime actions

An agent’s valid credentials do not guarantee that its next action matches the intended task or business policy.

Capabilities

From visibility to control.

Discover your AI attack surface

Scan source code to identify AI integrations and candidate endpoints. Map the models, prompts, tools, MCP servers, skills, and data connections that need review.

Test complete agent workflows

Use context-aware red teaming with single- and multi-turn attacks to uncover vulnerabilities. Examine the attack and response evidence to understand what failed.

Explore dForge

Create policies from findings

Generate policies informed by red-team findings, application context, and traffic analysis. Define boundaries suited to the agent’s role and your organisation’s requirements.

Enforce at runtime

Apply block, redact, flag, and lazy block actions through the AI Control Plane. Lazy block checks input in parallel with model generation: violations detected in time block the request; otherwise, it is flagged.

Explore dFence

Observe traffic and decisions

Use dFlow to inspect connected AI traffic, logs, and policy violation evidence. Understand agent behaviour and enforcement outcomes, and connect notifications to your team’s workflows.

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Govern prompts and model usage

Use dFlow to maintain model and prompt inventories and an MCP/tool registry. Route model requests dynamically, analyse token usage, and optimise costs within your security constraints.

Explore dFlow

A support request becomes an attempted data export.

01 / Risk
A prompt-injection test steers a support agent toward exporting customer records outside the user’s authorised task.
02 / Policy decision
The finding informs a policy that limits tool use to the intended support workflow and is applied at the connected enforcement point.
03 / Outcome
A subsequent test checks whether the policy blocks the attempted export. The team reviews the response and policy evidence to assess the result.

Sensitive information appears in a model response.

01 / Risk
An application’s response includes sensitive information that the user should not receive.
02 / Policy decision
A response policy checks connected output and applies redaction to content that violates the defined data-handling rules.
03 / Outcome
The sensitive content is redacted at the enforcement point, with the policy decision available in the logs.

A valid tool is used for the wrong task.

01 / Risk
An agent with access to a payment tool attempts an action outside the customer’s authorised workflow.
02 / Policy decision
An action policy evaluates the intended task, tool choice, and available request context before allowing the call.
03 / Outcome
The unauthorised action is blocked through the connected enforcement point. The team reviews the policy violation evidence.

Your context. Your policies.

Security policies tailored to your organisation.

Use application context, red-team findings, and available traffic analysis to inform policies that fit how your organisation works.

Dwaar’s in-house small language model (SLM) supports private policy evaluation. Choose deployment and data-handling boundaries suited to your environment, with control over sensitive policy inputs.

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