01 · Baseline your AI estate
A baseline assessment inventories every AI application in use, benchmarks maturity against the NIST AI Risk Management Framework, and produces a risk-ranked roadmap.
AI Security
AI Security Assessment
A structured entry point into AI security, a baseline risk assessment across your AI estate, active adversarial red team testing, and a dedicated review of the pipeline for any models you train or fine-tune yourself.
- 3 layers Baseline assessment, adversarial red team testing and model builder pipeline review, in one engagement path
- 2 to 6 weeks Per assessment layer, scoped to what you need
- MITRE ATLAS Every finding mapped to ATLAS and the OWASP LLM Top 10
The challenge
Roughly seventy percent of enterprise AI now operates outside formal IT oversight, which means most organizations cannot say with confidence what AI systems they run, whether those systems would hold up under an adversarial attack, or whether the models they train themselves were built on a secure pipeline.
Approach
How the engagement works
02 · Test it like an attacker
Red team engagements actively execute prompt injection, jailbreak, model extraction, and agent manipulation attacks to validate whether your defenses actually hold under realistic adversarial conditions and attack scenarios.
03 · Assess the build pipeline
For organizations training or fine-tuning their own models, a dedicated review covers training data provenance, MLOps access controls, supply chain integrity, model governance, secure deployment practices, and ongoing operational monitoring.
How it works
From an unknown AI estate to a validated, mapped risk picture
What’s included
- AI asset inventory and threat-model workshop mapped to OWASP LLM Top 10 and MITRE ATLAS
- NIST AI Risk Management Framework maturity scoring
- Active prompt injection, jailbreak and model extraction testing
- Agent and tool-use manipulation testing
- Training data provenance and MLOps pipeline security review
- Risk-ranked findings report and prioritized remediation roadmap
Outcomes
- A complete inventory of AI systems in use, not an assumption
- Defenses validated against real attack techniques, not just configuration checks
- A build pipeline reviewed for poisoning and supply chain risk before it becomes a production incident
Why Gruve
Most AI reviews check configuration and call it done
Gruve tests it, then reviews the pipeline behind it too
A configuration review tells you whether a guardrail is switched on. It does not tell you whether that guardrail survives an actual prompt injection attempt, or whether the model behind it was trained on a compromised pipeline. This assessment path covers all three layers, so nothing is assumed secure that has not been checked.
Gruve Differentiator
Gruve AI Security Assessment
Configuration Checklist Alone
Business Requirements
Organizations that want their AI estate inventoried, attacked, and pipeline-reviewed
Organizations that want a one-time configuration checklist
Service Model
Baseline, red team, and model builder review delivered as one path
A static review with no active testing behind it
Technology & Expertise
Active attack execution validates whether guardrails actually hold
Guardrails assumed effective because they are configured
Approach & Capabilities
Every finding mapped to MITRE ATLAS and OWASP LLM Top 10
Findings listed without a consistent framework mapping
Governance & Assurance
Training data and MLOps pipeline reviewed for organizations that build models
Build pipeline risk left unaddressed entirely
AI Security
Often deployed together
Managed AI Runtime Security
Real-time monitoring and blocking of prompt injection, jailbreaks and sensitive output at the inference layer.
Learn moreManaged AI Agent & MCP Security
Analysis and enforcement of agent privilege, tool-call chains and human-approval gates at the orchestration layer.
Learn moreManaged AI Data Security
Classification, DLP policy and audit logging for the data flowing through AI prompts and responses.
Learn moreTestimonials
Proof that AI guardrails work
not an assumption that they do
The partnership with Gruve brings significant value to customers by combining thought leadership, delivery, and execution of services. Leveraging AI/ML and Cloud tools in delivering software integrations and services can significantly ease transitions for large enterprise organizations.
Book your assessment
Start with a clear AI asset inventory before any testing begins
Know exactly how exposed your AI systems are before someone else finds out. Start an AI security assessment today.
- AI applications and data flows inventoried upfront
- Threat model and risk ranking completed before testing begins
- Red team and model builder review scoped based on baseline findings
Request a discovery call
A Gruve advisory lead will reach out within 1 business day.