AI Security Assessment & Planning
Review security, privacy, access-control, data-handling, and operational risks for proposed or existing AI systems.
Review security, privacy, access-control, data-handling, and operational risks for proposed or existing AI systems.
We help identify relevant risks, requirements, and potential controls for a defined AI use case. Penetration testing, managed monitoring, incident response, and other operational security services are provided only when separately agreed in writing.
Review model-access, intellectual-property, integrity, and adversarial-risk considerations.
Review sensitive-data handling, access controls, encryption requirements, retention, and potential leakage risks.
Identify relevant adversarial scenarios and define proportionate testing, review, and mitigation requirements.
Document governance, oversight, and compliance questions for review by the customer's qualified legal, security, and operational personnel.
A structured way to identify risks and define appropriate project requirements
Consider data provenance, access, retention, poisoning risks, and privacy requirements throughout the proposed lifecycle.
Consider model extraction, inversion, adversarial inputs, integrity, and access-control risks.
Secure the underlying infrastructure, APIs, and deployment environments for AI systems.
Modern AI systems face unique security challenges that require specialized protection approaches.
Sophisticated inputs designed to fool AI models into making incorrect predictions or classifications.
Attackers extract proprietary models through API queries to create replicas without training costs.
Malicious actors inject corrupted data during training to compromise model performance and behavior.
Attackers determine whether specific data was used in training, potentially revealing sensitive information.
Reconstruction of training data from model outputs, potentially exposing confidential information.
Techniques that cause AI-based classification or automated-routing systems to produce incorrect or unintended results.
Security outcomes depend on system design, data, controls, deployment, and ongoing operations
Threat Exposure
Data and Access Controls
Failure and Abuse Scenarios
Operational Signals
The relevant workstreams depend on the system, data, environment, risk profile, and agreed written scope.
Assess adversarial scenarios and define appropriate robustness testing and review methods.
Evaluate whether privacy-preserving approaches such as federated learning or differential privacy are appropriate for the use case.
Evaluate watermarking or provenance options, together with their technical limitations and evidentiary requirements.
Review relevant AI-specific threat categories, known vulnerabilities, and appropriate sources of current security information.
Establish frameworks for ethical AI use, regulatory compliance, and risk management.
Comprehensive testing for AI system vulnerabilities and security weaknesses before deployment.
Custom engagements may follow this process. Included stages and responsibilities are confirmed in a separate written scope.
We review available system information to identify relevant risks, assumptions, and areas requiring further specialist testing.
When included in scope, we document proposed controls across data, models, infrastructure, access, and operations.
Control implementation and integration may be provided under a separately defined custom development scope.
Testing methods and qualified providers are selected according to the agreed scope. Penetration testing is not included unless expressly stated in writing.
Monitoring and detection requirements can be designed as part of a separately scoped implementation project.
Ongoing maintenance or security support is available only under a separate written scope, service period, and responsibility model.
Examples of common AI-enabled workflows where access control, data handling, testing, monitoring, and human oversight should be designed into the solution.
Assess source permissions, user access, retrieval boundaries, output logging, and confidential-data exposure risks in internal knowledge tools.
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Review document access, retention, extraction validation, integration permissions, and human-review controls for AI-assisted intake and routing.
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Evaluate prompt-injection, account-access, data-exposure, response-quality, and escalation controls for support knowledge and response-drafting systems.
Learn MoreContact us to discuss an assessment or a separately scoped implementation project.
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