SYMOSIS
Secure AI Adoption Starts With Security by Design
We help organizations understand, govern, test, and secure AI applications, agents, identities, and operations before risk becomes an incident.
From Copilots and RAG platforms to autonomous agents and AI-enabled SaaS, Symosis helps security leaders establish practical controls without slowing responsible innovation.
Tested against real attack paths. Designed for real operating environments.
AI Security Advisory Services
AI Application & Agent Security
Assess and test LLM, RAG, Copilot, and agentic applications across prompts, models, memory, tools, APIs, and business logic.
- Prompt injection and jailbreak testing
- RAG and sensitive-data exposure
- Unsafe tool use and excessive agency
- Authorization bypass and business-logic abuse
HOW WE DO IT
- Inventory models, agents, tools.
- Architecture and flow review.
- Abuse case building.
- Testing & documentation.
TYPICAL OUTPUTS
- AI threat model
- Risk-ranked findings
- Remediation roadmap
AI Identity & Authorization
Assess who—and what—can invoke AI systems, access data, and take actions across the environment.
- AI agent identity inventory
- OAuth and API scope review
- Least-privilege analysis
- Human approval attribution
HOW WE DO IT
- Inventory agents & keys.
- Map identities to owners.
- Test privilege escalation.
- Design expiration rules.
TYPICAL OUTPUTS
- Permission map
- Target IAM model
- Backlog implementation
AI Security Architecture
Review and design secure architectures for Copilot, LLM, RAG, and AI-enabled SaaS environments.
- AI data-flow and trust review
- Vector store and API security
- Data protection & isolation
- Secure deployment patterns
HOW WE DO IT
- Review integrations & agents.
- Identify data entry points.
- Define network controls.
- Roadmap production.
TYPICAL OUTPUTS
- Architecture diagrams
- Threat model analysis
- Implementation plan
AI Security Operations & Validation
Validate whether the SOC can detect, contain, and learn from AI-specific threats and unsafe behavior.
- AI telemetry & logging review
- SIEM detection use cases
- SOC playbook validation
- Control effectiveness testing
HOW WE DO IT
- Identify AI telemetry.
- Design prompt detections.
- Review triage paths.
- Tabletop exercises.
TYPICAL OUTPUTS
- Coverage assessment
- Response playbooks
- Monitoring roadmap
What We Help You Answer
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What AI applications, agents, and non-human identities exist in our environment?
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What data can each AI system access?
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Can an attacker manipulate the model, agent, tools, or connected APIs?
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Are permissions excessive, undocumented, or difficult to revoke?
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Can the SOC detect prompt injection, unsafe behavior, and abnormal agent activity?
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What controls and evidence do we need for governance, risk, and compliance?
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Is our AI security architecture ready for production use?
Typical Engagements
AI Security Launch Assessment
A focused assessment of AI applications, agents, identities, data access, attack paths, and operational controls.
Secure AI Architecture Review
Architecture and control design for AI applications, copilots, RAG platforms, and agentic workflows.
AI Red Team & Abuse Simulation
Adversarial testing of LLMs, RAG systems, copilots, agents, tools, APIs, and business workflows.
AI Security Monitoring Validation
Validation of telemetry, detection logic, response playbooks, control effectiveness, and audit evidence.
AI Agent Identity Review
Assessment of non-human identities, permissions, OAuth scopes, delegated access, ownership, and least-privilege controls.
Our Advisory Method
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Discover
Identify AI applications, agents, data flows, identities, integrations, owners, and operating context.
Assess
Test security, privacy, authorization, resilience, governance, and operational readiness.
Prioritize
Translate findings into business risk, attack scenarios, control priorities, and practical remediation steps.
Enable
Provide the roadmap, architecture, policies, playbooks, evidence, and engineering handoff required to move forward.
Frameworks and Security References
NIST AI Risk Management Framework
MITRE ATLAS
Zero Trust principles
OWASP Top 10 for LLM Applications
ISO/IEC 42001
EU AI Act
NIST Cybersecurity Framework
Cloud and identity security best practices
Who We Help
CISOs and security executives
AI governance and risk leaders
CIOs and enterprise architects
Application and platform engineering teams
Cloud security and identity teams
SOC and incident response leaders
Legal, privacy, and compliance stakeholders
What You Receive
AI asset and agent inventory
Security assessment findings
AI threat model and attack-path analysis
Identity and authorization risk analysis
Red-team and abuse-testing results
Prioritized remediation roadmap
Target-state security architecture
Monitoring and incident-response requirements
AI security policies and control recommendations
Executive-ready risk summary
Make AI Adoption Safer and More Defensible
Whether you are deploying Copilot, building an AI application, or preparing for agentic workflows, Symosis can help you understand the risk and establish the controls required for responsible adoption.