Symosis assesses how AI applications, copilots, and agents interact with enterprise data, identities, tools, and business processes.
Our senior practitioners deliver evidence-based findings, clear priorities, and practical direction for governance, architecture, implementation, and operations.
Establish visibility, ownership, and proportionate oversight.
We identify AI use cases, review available discovery evidence, assess risk, and define intake, approval, exception, and reassessment processes.
We also review relevant vendor evidence and help develop policies that connect to technical controls.
Typical outputs
AI inventory, risk register, governance model, policies, and prioritized actions.
Assess how models, applications, retrieval systems, APIs, and infrastructure connect.
We map trust boundaries and data flows, review retrieval authorization, and examine sensitive-data handling across prompts, outputs, logs, and connected services.
Typical outputs
Threat model, architecture findings, data-exposure assessment, and security requirements.
Determine the authority behind agent actions.
We review service identities, delegated access, OAuth scopes, credentials, MCP integrations, tool permissions, and approval requirements.
The assessment considers attribution, access revocation, and enforcement by the systems executing actions.
Typical outputs
Identity inventory, permission matrix, approval requirements, and privilege-reduction plan.
Evaluate agreed adversarial scenarios across the application and its connected systems.
Testing can examine prompt injection, unauthorized retrieval, tool misuse, access-boundary failures, and unsafe downstream handling.
Typical outputs
Evidence-based findings, attack-path analysis, and remediation recommendations.
Assess whether teams can observe AI activity, investigate suspicious behavior, and respond.
We review telemetry, detection requirements, incident ownership, containment options, and validation practices as systems change.
Typical outputs
Coverage assessment, monitoring requirements, response playbooks, and assurance plan.
We combine stakeholder discussions with available architecture, configuration, permission, integration, and operational evidence.
Targeted testing is defined in the engagement scope. Findings distinguish observed weaknesses, design concerns, and areas requiring further validation.
Scope
Agree on use cases, systems, objectives, and assessment boundaries.
Discover
Map data, identities, tools, owners, and dependencies.
Assess
Evaluate controls and perform agreed technical analysis.
Prioritize
Connect findings to business impact and remediation dependencies.
Enable
Deliver the roadmap and review implementation requirements with your teams.
Deliverables may include:
Symosis can help implement the recommended controls and validate their behavior.
Client evidence · pending approval
A sanitized technical example, sample deliverable or approved case study for this service will appear here once approved. No results are shown until then.
It is the set of policies, ownership, risk criteria and controls that determine which AI uses are approved, how they are reviewed and how they are monitored over time.
Most organizations start with an AI inventory and use-case risk review, because every later control decision depends on knowing what AI is in use and what data it touches.
Give each agent a distinct identity, grant least-privilege scoped access, require approval for high-impact actions, and log every action with the originating user and agent.
No. It applies equally to organizations consuming third-party AI services, copilots and SaaS features with embedded AI.
Tell us which AI systems you use, what they connect to, and where your organization needs confidence.