DATA
Sensitive-data leakage
Personal, confidential or regulated information can surface through prompts, responses, memory, retrieval, exports or logs beyond the intended audience.
Artificial Intelligence · AI Assurance
Privacy and Security Testing for AI Systems Before Sensitive Data or Actions Reach Production
DataConsultant assesses generative AI applications, RAG workflows, model APIs, agents, connected tools and supporting data flows for privacy leakage and security weaknesses. The engagement converts risk hypotheses into authorised test scenarios, reproducible evidence, prioritised findings and practical remediation decisions.
✓AI-specific privacy and attack scenarios
✓Evidence-backed findings and severity rationale
✓RAG, agent, tool and model-boundary coverage
✓Remediation guidance and optional retesting
Testing is performed only within agreed, authorised boundaries. A service assessment does not guarantee complete security, universal privacy compliance or absence of future misuse.
AI Privacy & Security Test MatrixControlled testing
ScopeAssets, data, users
Threat modelAbuse and leakage paths
TestAuthorised scenarios
EvidenceTrace and reproduce
RetestVerify remediation
Illustrative finding registerEvidence linked
Indirect prompt path reaches restricted contextPrioritise
Session data remains visible beyond intended boundaryReview
Tool approval control blocks unauthorised actionEffective
Decision-ready outputWhat failed, what evidence supports it, who owns remediation, what should be retested and which residual risks need an explicit decision.
AI-specificTests the prompt, retrieval, model, agent and tool boundaries that conventional checks can miss.
Evidence-ledFindings are tied to reproducible scenarios, observed behaviour and clearly stated limitations.
AuthorisedEnvironment, access, permitted techniques, stop conditions and data handling are agreed before execution.
Remediation-focusedOutputs connect each material weakness to ownership, priority, control options and retest criteria.
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AI Privacy and Security Failures Often Cross Data, Model and Application Boundaries
An AI feature can appear to work while still creating hidden exposure through retrieved context, system prompts, logs, third-party model handling, excessive agent permissions or connected business tools. The test plan focuses on the paths that matter to the intended use and risk profile.
PROMPT
Instruction and context manipulation
Direct or indirect prompt injection can alter system behaviour, reveal restricted context or influence downstream tool calls.
ACCESS
Weak identity and permissions
AI workflows may inherit broad user, service-account or tool privileges that do not match the business action being requested.
RAG
Retrieval boundary failures
Indexes, vector stores and retrieval filters can expose cross-user, cross-tenant or restricted documents when metadata and authorisation controls are weak.
TOOLS
Unsafe tool and connector paths
Agents and copilots can call APIs, functions or enterprise systems with invalid arguments, insufficient approvals or excessive data access.
EVIDENCE
Incomplete monitoring and traceability
Insufficient logs, version context or decision records can make misuse, leakage, remediation and release decisions difficult to investigate or defend.
Identify the AI Surfaces That Need Testing Before You Expand Access or Autonomy
Share the system purpose, architecture, data sensitivity and planned deployment decision. DataConsultant can help shape a risk-based test boundary rather than applying a generic checklist.
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Move From Ad Hoc AI Checks to Governed Privacy and Security Evidence
The goal is not a longer vulnerability list. It is a clearer release and remediation decision supported by traceable evidence, accountable owners and an agreed retest path.
Fragmented assurance
- AI risks reviewed separately by product, privacy and security teams
- Prompt and RAG testing depends on informal demonstrations
- Tool permissions are not connected to agent behaviour tests
- Evidence is difficult to reproduce across model or prompt changes
- Release decisions rely on assumptions about third-party AI boundaries
Traceable, risk-based testing
- Threat hypotheses link business impact to specific test scenarios
- Privacy, security and AI control evidence is reviewed together
- Findings include reproduction evidence, ownership and priority rationale
- Retest criteria make remediation verification repeatable
- Residual risk and limitations are explicit for accountable decision-makers
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What the Privacy and Security Testing Service Covers
Coverage is tailored to the AI architecture and decision in scope. A focused assessment may test one production-bound workflow; a broader engagement may combine several models, RAG components, agent tools, environments and user roles.
Data inventory and flow
Trace personal, confidential and sensitive information across inputs, prompts, retrieval, model calls, outputs, logs, memory, exports and external processors.
Prompt and response boundaries
Test system instructions, contextual controls, policy bypass, leakage, unsafe output handling and selected prompt-injection paths.
RAG and vector stores
Review retrieval filters, document permissions, metadata, poisoned context, cross-user exposure and handling of restricted source material.
Identity and access
Assess authentication, authorisation, role boundaries, service identities, tenant separation and least-privilege alignment around AI actions.
Agents and tool permissions
Evaluate tool selection, permitted actions, approval gates, argument validation, unsafe chaining, state handling and recovery from failures.
APIs and integrations
Review AI-facing application interfaces, secrets handling, connector trust, input validation, outbound data paths and selected business-logic controls.
Provider and model boundary
Document model-hosting assumptions, data-use settings, retention dependencies, vendor constraints, model changes and evidence outside direct client control.
Logging and incident evidence
Assess whether relevant AI events, tool actions, versions, identities and exceptions can support investigation, remediation and ongoing monitoring.
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Assessment Dimensions Connect AI Behaviour to the Controls Around It
The dimensions below create a practical test map across the system. They are not a generic maturity score and are adjusted to the deployed architecture.
01Assets & ownershipSystems, models, data, owners and decision rights
02Data protectionSensitivity, minimisation, access, retention and transfer
03Prompt controlsInstructions, policies, injection paths and output use
04RetrievalSource permissions, filters, poisoning and context leakage
05IdentityUser, tenant, service-account and role boundaries
06Agents & toolsPermissions, approvals, arguments and autonomous actions
07Model endpointProvider boundary, keys, routing and model-version assumptions
08Application securityAPIs, connectors, validation, sessions and business logic
09Secrets & credentialsExposure paths, storage, rotation and least privilege
10ObservabilityLogs, traces, alerts, version context and investigation evidence
11Third partiesProcessors, model services, plugins and supply-chain dependencies
12Response & recoveryContainment, kill switches, escalation, retest and residual risk
Need One Test Plan Across RAG, Agents, APIs and Sensitive Data?
Bring the architecture and the deployment decision. We can define which components, user roles, data paths and attack scenarios belong in the same assurance boundary.
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Test the Complete AI Path, Not Only the Chat Interface
Privacy and security weaknesses can appear before the model call, inside retrieved context, at the model boundary or after an agent reaches a business system. The architecture path helps keep test coverage connected to real data and action flows.
User & identityAuthentication, roles, tenant boundary, session and user context
Application & promptInput handling, system instructions, policy and prompt injection
Retrieval & memoryDocument access, vector search, context, history and persistence
Model & providerEndpoint security, model routing, retention and provider dependencies
Tools & actionsAPIs, permissions, approvals, arguments, execution and recovery
Logs & feedbackEvidence, monitoring, exports, incident signals and regression data
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Convert Test Evidence Into Findings, Control Decisions and Retest Criteria
Observed weaknesses are documented with enough context to reproduce the issue, understand the affected boundary, assign an owner and decide what evidence is needed to close or accept the risk.
Illustrative findings view
Observed conditionRiskEvidenceNext action
Retrieved context ignores intended document boundaryHighReproducibleFix filter + retest
Tool action requires additional approval for elevated roleMediumTrace capturedTighten policy gate
Sensitive action blocked and recorded by controlControlTrace capturedRetain evidence
Illustrative structure only. Actual severity, evidence and remediation depend on the scoped system and observed behaviour.
Responsible control view
AI
Control
Data & privacyIdentity & accessModel & promptMonitoring & incidentHuman approvalTools & connectorsControl
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Deliverables Built for Product, Security, Privacy, Risk and Executive Decisions
The output package is shaped around the decision you need to make: release, procurement, remediation, risk acceptance, change approval or establishment of an ongoing assurance process.
01
Scope and authorised test plan
Systems, environments, roles, permitted techniques, exclusions, evidence requirements and stop conditions.
02
Architecture and data-flow view
AI components, model/provider boundaries, retrieval, tools, data movement and ownership assumptions relevant to testing.
03
Threat model and scenario register
Risk hypotheses linked to plausible misuse, privacy exposure, security failure and control-bypass paths.
04
Evidence-backed findings
Observed behaviour, reproduction context, affected boundary, severity rationale, limitations and supporting traces or artefacts.
05
Prioritised remediation backlog
Recommended control changes, accountable owners, dependencies, sequencing and retest acceptance criteria.
06
Executive assurance readout
Material findings, residual risk, decision implications, limitations and recommended next steps for accountable stakeholders.
07
Privacy and control mapping
Evidence mapped to selected policy, privacy, security or AI risk requirements where that mapping is explicitly in scope.
08
Retest evidence
Verification of agreed fixes against reproducible findings when remediation retesting is commissioned.
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Delivery Method: From Authorised Scope to Remediation Evidence
The sequence can be adapted to procurement, pre-release, change assurance or incident follow-up. Timeline is confirmed after scoping rather than assumed from a standard package.
Scope
Agree systems, decisions, access, data handling, constraints and success criteria.
Evidence
Review architecture, identities, data flows, configurations, policies and known concerns.
Threat model
Translate business impact and architecture into testable misuse and leakage hypotheses.
Test
Execute authorised representative, boundary and adversarial scenarios with evidence capture.
Prioritise
Validate findings, rate materiality and connect issues to owners and remediation options.
Retest
Verify agreed remediation and document residual risk, limitations and next controls.
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What We Need From Your Team to Test the Right Boundaries
Complete evidence is not always available at the start. Missing information is recorded as a limitation rather than silently assumed.
System context
Business purpose, intended users, impact, release decision, architecture diagrams and environments.
AI configuration
Models, providers, system prompts, RAG design, agents, tool definitions, policies and change history.
Data context
Data classifications, retrieval sources, personal or confidential data, retention and jurisdiction constraints.
Access and integrations
Test accounts, roles, APIs, service identities, tools, connectors, secrets approach and approval paths.
Control evidence
Policies, security controls, privacy requirements, incident history, prior findings, logs and monitoring information.
Accountable stakeholders
Product, engineering, AI, security, privacy, legal or risk contacts who can validate context and decisions.
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Map Testing Evidence to the AI, Security and Privacy References That Matter
Framework mapping can help security, privacy, risk and audit teams interpret findings in a common language. References are selected during scoping and do not create a certification or legal-compliance guarantee.
OWASP GenAI LLM Top 10 2026
Useful for current generative-AI security risk categories such as prompt injection, sensitive information disclosure, excessive agency and unsafe application handling.
OWASP Top 10 for Agentic Applications 2026
Relevant where agents plan, persist state, use tools or take actions that create additional identity, permission and control-boundary risks.
NIST AI RMF and Generative AI Profile
Can frame governance, measurement, risk treatment, transparency and generative-AI considerations around the technical evidence collected.
DPDP Act 2023, DPDP Rules 2025 and NIST Privacy Framework
Where applicable, privacy observations can be mapped to data-handling, accountability and risk-management questions without substituting for legal advice.
Important boundary: DataConsultant can document observed controls, gaps and evidence against agreed criteria. Legal interpretation, statutory audit, formal certification and regulator-facing opinions require the appropriately qualified or authorised party and are not implied by this testing service.
Need Findings That Engineering Can Reproduce and Risk Teams Can Use?
Scope the engagement around evidence quality, ownership and retest criteria so privacy and security observations translate into practical remediation decisions.
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When This Service Is the Right Assurance Intervention—and What Requires Separate Scope
A clear boundary avoids treating every privacy, application-security or compliance requirement as the same engagement.
Use privacy and security testing when
- An AI application, RAG workflow or agent is approaching production or wider rollout
- A material model, prompt, retrieval, tool or architecture change needs independent evidence
- Sensitive, personal, confidential or regulated information is involved
- Procurement or vendor review needs a documented view of AI-specific risk
- An incident or near miss has exposed uncertainty about data or control boundaries
- Security, privacy, product and risk teams need a shared remediation register
Not automatically included
- Statutory audit, formal certification, legal opinion or regulator attestation
- Destructive production testing or techniques outside expressly authorised boundaries
- Broad network, infrastructure, mobile or enterprise penetration testing unrelated to the AI workflow
- Full software remediation, code rewrite or platform implementation
- A formal DPIA or jurisdiction-specific legal assessment unless explicitly commissioned with the right specialists
- Continuous managed monitoring after the agreed test and retest window
12
Commercial Clarity: Privacy and Security Testing Is Quoted Against the Real AI Attack Surface
No approved fixed DataConsultant fee has been published for this service. Public market pricing for adjacent AI penetration testing, red teaming and security assessments varies materially in depth and does not reliably represent a combined enterprise privacy-and-security assurance scope, so DataConsultant pricing is confirmed after scoping.
Request a Quote
The quote is based on the systems, roles, environments, data paths, test depth and evidence package required to support your decision. It is not created by applying a generic per-model or per-prompt rate.
Pricing basisScope-led, confirmed after discovery
TimelineConfirmed after scoping
RetestingIncluded only when agreed in scope
Commercial outputWritten scope, assumptions and quote
01AI surfacesNumber of applications, models, RAG flows or agents
02Architecture depthRetrieval, memory, tools, orchestration and model boundaries
03Roles & tenantsUser types, privileges, service identities and separation tests
04Data sensitivityPersonal, confidential, regulated and cross-border data context
05IntegrationsAPIs, plugins, enterprise systems, tools and third parties
06Test depthRepresentative, boundary, adversarial and manual review effort
07Environment accessTest accounts, staging availability, provider constraints and approvals
08Evidence & mappingReport detail, control mapping, executive readout and audit support
09Retest & supportRemediation verification, regression assets and follow-on assistance
Get a Quote Based on Your AI Architecture, Data Sensitivity and Test Depth
Provide a high-level description of the system, environments, integrations and decision deadline. We can clarify scope factors before a commercial proposal is prepared.
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Why DataConsultant’s Approach Fits Cross-Functional AI Assurance
The service is structured around the business decision, the AI architecture and the evidence required by product, engineering, privacy, security and governance stakeholders.
Business-led test scope
Testing starts with the intended use, affected data, material actions and release decision rather than a generic vulnerability list.
AI and application boundaries together
Coverage can connect prompts, RAG, models, agents, identities, APIs, tools and evidence across the complete workflow.
Evidence before conclusions
Findings distinguish observed behaviour, assumptions, inaccessible boundaries and limitations so buyers can judge confidence appropriately.
Remediation traceability
Material findings are connected to ownership, remediation choices and the evidence needed to verify closure.
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Privacy and Security Testing for AI Systems: Buyer FAQs
Answers cover scope, architecture, privacy, security, standards, deliverables, inputs, timeline, pricing and follow-on assurance.
What is privacy and security testing for AI systems?
It is a scoped assurance engagement that evaluates how an AI application, model workflow, retrieval layer, agent, connected tool and supporting data flow could expose sensitive information or permit unauthorised behaviour. Testing is authorised, evidence-led and bounded by the agreed environment, scenarios and access.
Which AI systems can be included in scope?
Scope can cover generative AI applications, large-language-model workflows, retrieval-augmented generation, copilots, AI agents, model APIs, custom machine-learning services and AI-enabled enterprise workflows. The exact test surface depends on architecture, ownership, provider constraints and available test access.
What privacy risks can the engagement assess?
Privacy testing can examine personal or confidential data exposure through prompts, responses, retrieval, memory, logs, exports, caches and connected tools; excessive data collection; weak data minimisation; role or purpose mismatch; retention and deletion dependencies; third-party processing; and evidence needed for applicable privacy reviews.
What AI security weaknesses can be tested?
Depending on scope, testing can address direct and indirect prompt injection, data exfiltration paths, insecure output handling, weak authentication or authorisation, excessive agent permissions, secrets exposure, RAG or vector-store access issues, unsafe tool invocation, API and connector controls, logging gaps and selected supply-chain or model-provider dependencies.
Do you test prompt injection, RAG and AI agents?
Yes, when those components are in scope and authorised for testing. The test design can include direct and indirect prompt injection, malicious retrieved content, context leakage, cross-user or cross-tenant access paths, agent tool abuse, unsafe actions and approval-control bypass scenarios.
Does the service prove compliance with the DPDP Act or another privacy law?
No. The engagement can map observed data flows, controls, evidence and gaps to relevant privacy requirements, including India’s Digital Personal Data Protection Act 2023 and the Digital Personal Data Protection Rules 2025 where applicable. It does not replace legal advice, a statutory audit or a formal compliance opinion.
Which AI security and risk frameworks can inform the test plan?
Relevant references can include the OWASP GenAI LLM Top 10 2026, the OWASP Top 10 for Agentic Applications 2026 for agentic systems, the NIST AI Risk Management Framework, the NIST Generative AI Profile and the NIST Privacy Framework. Final criteria are selected for the system, use case and decisions in scope rather than applied as a generic checklist.
What deliverables will we receive?
Typical outputs can include an agreed scope and test plan, architecture and data-flow view, threat model, test-scenario register, evidence pack, prioritised findings with severity rationale, privacy and security observations, remediation backlog, residual-risk notes, retest results where commissioned and an executive readout.
What information do you need from our team?
Useful inputs include architecture diagrams, model and provider details, prompt and agent configurations, RAG and vector-store design, data classifications and flows, identity and access models, tool and API inventories, logging information, test accounts, relevant policies, known incidents or concerns, and access to accountable product, security, privacy and engineering stakeholders.
Can testing be performed safely against production systems?
Production testing is not assumed. The preferred environment, permitted techniques, traffic limits, data handling, credentials, escalation path and stop conditions are agreed before execution. Destructive or high-impact techniques require explicit authorisation and may be excluded in favour of a controlled test environment.
How long does an AI privacy and security testing engagement take?
A reliable timeline is confirmed after scoping. Duration depends on the number of AI surfaces, architecture complexity, model and vendor boundaries, integrations, user roles, data sensitivity, test depth, environment readiness, evidence requirements, stakeholder availability and whether remediation retesting is included.
How is privacy and security testing priced?
DataConsultant does not publish a fixed fee for this service. Pricing is scope-led and confirmed through a Request a Quote process after the number of AI systems and environments, architecture patterns, integrations, test depth, data sensitivity, evidence requirements, regulatory mapping, onsite needs and retesting expectations are understood.
Is this the same as a conventional penetration test?
Not necessarily. Conventional penetration testing may be part of the wider security programme, but this service is centred on AI-specific privacy and security failure modes across prompts, retrieval, model boundaries, agent actions, tools, data flows and governance evidence. A broader infrastructure, network or application penetration test should be separately scoped when required.
Can DataConsultant retest after remediation or support ongoing assurance?
Yes. Retesting can be scoped to verify agreed remediation against reproducible findings. Follow-on support can also include regression-test design, continuous AI evaluation, control improvement, evidence refresh and coordination with internal product, security, privacy, risk and engineering teams.
Start with the system and decision
Tell Us What You Need to Protect, Test or Prove Before AI Deployment
Share enough context for us to understand the AI workflow and the decision the testing must support. Do not submit passwords, private keys, confidential credentials or unnecessary sensitive personal information through this form.
- AI application, model, RAG or agent in scope
- Business use case and deployment stage
- Types of sensitive or personal data involved
- Connected APIs, tools or enterprise systems
- Known privacy, security or assurance concerns
- Desired decision: release, procurement, remediation or retest
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Request a Privacy & Security Testing Scope Review
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