Prompt Injection
Instructions cross a trust boundary.
Enterprise AI Security & Assurance
Put autonomous AI into production—without losing control.
Orbyntis helps CIOs, CISOs, and technology leaders make agent authority visible, controls testable, and every critical action reviewable.
Authority VisibleControls TestedEvidence Reviewable
Approved Technology Partners
MicrosoftAI Cloud Partner
Orbyntis AI Security LabSee where autonomous authority becomes exposure.Explore one architecture through 15 private, in-browser questions. Directional, not a certification.
Guided Exposure Check
One workflow.
Five control layers.
Answer from the perspective of the highest-impact agent you operate or plan to deploy.
- 01Context
- 02Authority
- 03Reach
- 04Controls
- 05Evidence
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Directional Exposure Index
0/100
Exposure mapped
Calculated only from the 15 answers above. This is not a vulnerability scan, certification, or compliance determination.Threat Exposure Map
Which paths are relevant to your architecture?
Select any combination. The map shows exposure paths to review—it does not claim that a selected platform is vulnerable.
Platforms & Frameworks
Agent Capabilities
Selected ArchitectureChoose your stack aboveSelections stay in this browser.
ORBYNTIS CONTROL VIEWAUTHORITYPOLICYEVIDENCE
Relevant Exposure Paths0
Select a platform or capability to begin.
Indirect Injection
External content influences agent behavior.
Tool Abuse
A valid tool is used outside intended purpose.
Unsafe Execution
Actions change systems without a sufficient gate.
Data Leakage
Sensitive context reaches an unintended destination.
Memory Poisoning
Stored context changes later decisions.
Agent Escalation
Delegated authority exceeds the initiating scope.
Identity Spoofing
Action ownership becomes ambiguous.
Privilege Escalation
Agent rights exceed least privilege.
Model Jailbreak
Policy behavior is intentionally bypassed.
Supply-Chain Risk
External models, tools, or content alter trust.
MCP Risk
Server authorization and tool scope need review.
Tool Poisoning
Tool metadata or behavior becomes hostile.
Evidence Gap
Critical actions cannot be reconstructed.
What Changes Before AI Acts
Make authority visible.
Make control provable.
Three questions matter before an autonomous system reaches production.
Know the boundary
Test the pressure
Prove the control
ProductsFive products. One assurance path.Start with the risk you need to understand. Expand only when you are ready.
Assess exposure before AI acts.
Map permissions, tools, memory, identity, and action boundaries—then test where control can fail.
Explore AgentShieldKeep assurance evidence reviewable.
Organize the evidence created through assessment, validation, and operational review.
Explore Evidence VaultMake ownership and approval explicit.
Connect AI actions to clear decision rights, review points, and accountable operating roles.
Explore GovernTest assurance as systems change.
Bring repeatable control validation into the delivery path before higher-risk changes move forward.
Explore CI GateKeep authority visible in operation.
Support runtime review and response around sensitive tools, actions, and business workflows.
Explore Runtime GuardServices
Engineering depth where
assurance needs it.
Explore only the service relevant to your immediate risk.
AI Engineering
Enterprise AI Consultation & Engineering
Plan and engineer enterprise AI workflows with clear security boundaries, operating controls, and production-readiness criteria.
Challenge
A working AI pilot still needs explicit data boundaries, approval paths, and production acceptance criteria.
Engagement Outputs
- A documented target architecture and trust-boundary map
- Prioritized engineering and security requirements
- A controlled path from pilot to production
AI Security Testing
AI Red Teaming
Test AI agents and autonomous workflows for prompt injection, tool misuse, data exposure, control bypass, and unsafe business actions.
Challenge
Model-only tests can miss prompt injection, tool misuse, and approval bypass across the complete action chain.
Engagement Outputs
- A threat-informed adversarial test plan
- Evidence-backed findings ranked by business impact
- Remediation guidance and validation criteria
Agent Security
Agent Security & Runtime Risk Assessment
Assess AI agent permissions, tools, memory, context, identity, approvals, and runtime controls before business-critical deployment.
Challenge
An agent's identities, tools, memory, and integrations can give it more authority than its owners expect.
Engagement Outputs
- An inventory of agent capabilities and trust boundaries
- A risk-ranked control-gap assessment
- Recommended runtime, approval, and containment controls
Secure Engineering
Secure Software Development
Engineer security-conscious AI applications, integrations, automation, and remediation for enterprise environments.
Challenge
Assessment findings need reliable implementation across application logic, identity, APIs, and review interfaces.
Engagement Outputs
- Security requirements translated into working software
- Reviewable integration and control patterns
- Validation evidence for implemented remediation
AI Governance
AI Governance, Observability & Compliance Advisory
Translate enterprise AI risk into accountable governance, operational evidence, monitoring, and compliance-ready review practices.
Challenge
Policies need defined owners, observable controls, and evidence from the systems they govern.
Engagement Outputs
- Clear ownership and approval responsibilities
- Defined runtime evidence and review requirements
- A governance model connected to engineering controls
Enterprise AI Consultation & Engineering
How It Works
A practical path from unclear autonomy to controlled operation.
Orbyntis work starts by making the agent boundary explicit, then turns risk into engineered controls, validation evidence, and operating ownership.
Assess
Map authority
Define the agent, users, identities, data, tools, approvals, and business actions in scope.
Step Output
Workflow boundary and authority inventory
What You Leave With
- A defined agent authority boundary
Threat Model
Find misuse paths
Connect prompt, context, identity, tool, and approval risks to business impact.
Step Output
Threat scenarios and control assumptions
What You Leave With
- Prioritized misuse paths and control assumptions
Engineer
Build controls
Place deterministic checks around sensitive actions instead of relying only on model behavior.
Step Output
Control plan and remediation backlog
What You Leave With
- Engineering actions tied to business risk
Validate
Prove the change
Exercise realistic failure paths and confirm controls hold under adversarial or degraded conditions.
Step Output
Test evidence and validation results
What You Leave With
- Validation evidence that can be reviewed
Operate
Review evidence
Keep ownership, logging, containment, and reassessment tied to material workflow changes.
Step Output
Operating cadence and evidence trail
What You Leave With
- Operating ownership for monitoring and change
Map authority
Learn AI Security
Move from concern to a testable risk boundary.
Use these evergreen guides to understand autonomous AI threats, agent security decisions, enterprise patterns, and common assurance questions.
Agent Foundations
AI Agent Security
A practical hub for authority, tools, identity, context, controls, and runtime evidence.
Topics Covered
- Agent identities and permissions
- Tool access and context boundaries
- Runtime controls and reviewable evidence
Threat Research
Autonomous AI Threat Library
Threat families for agentic workflows, mapped to business impact and control questions.
Topics Covered
- Prompt injection and poisoned context
- Excessive permissions and unsafe tool use
- Approval bypass and audit gaps
Connected Tools
MCP Security
Server, tool, testing, prompt injection, and data exposure guidance for MCP-connected agents.
Topics Covered
- MCP server and tool security
- Prompt injection and data exposure
- Testing connected agent workflows
Retrieval Security
RAG Security
Retrieval testing, prompt injection, and data leakage guidance for enterprise AI systems.
Topics Covered
- Retrieval trust boundaries
- Indirect prompt injection
- Data leakage and security testing
Common Questions
AI Security FAQ
Short answers to common questions about agent security, red teaming, approvals, and evidence.
Topics Covered
- Agent security and red teaming
- Human approvals and ownership
- Evidence and assurance decisions
Enterprise Patterns
Enterprise Case Studies
Anonymous patterns from AI risk, red teaming, governance, and assurance work.
Topics Covered
- Enterprise context and assessment scope
- Control areas and deliverable patterns
- Lessons that generalize without exposing customer details
AI Agent Security
Bring us the system
you need to trust.
Start with a focused briefing. Leave with a clearer assurance path.
Request a Briefing