CISOs & Security Leaders
Understand your organization's AI security exposure and prioritize investments.
AI Security Services
Security for the AI Systems You Are Building and Deploying
AI introduces new security risks across applications, agents, models, APIs, data, infrastructure, and software supply chains.
Arapearly AI Security helps organizations identify those risks, understand their potential impact, and build practical controls before deployment.
Request an AI Security AssessmentOur Services
AI Security Assessments
Primary service
Find Security Gaps Before Attackers Do.
Our AI Security Assessments provide an independent evaluation of your AI environment, identifying vulnerabilities, attack paths, security gaps, and areas requiring additional controls.
We assess the technology, architecture, data flows, identities, applications, agents, APIs, and AI supply chain components that support your AI environment.
We Assess
- AI applications
- LLM integrations
- AI agents
- MCP implementations
- RAG pipelines
- APIs
- Identity and access
- Data flows
- Cloud infrastructure
- AI supply chain
- Security controls
You Receive
- Executive risk summary
- Architecture review
- AI threat model
- Security findings
- Risk ratings
- Remediation recommendations
- 30/60/90-day roadmap
- Executive presentation
AI Agent Security
Secure AI Agents Before They Take Action.
AI agents can interact with applications, APIs, databases, files, and other tools.
The more authority an agent has, the greater the potential impact of a compromised or manipulated agent.
We evaluate the security of your agentic systems and identify risks associated with autonomous actions and tool access.
We Assess
- Agent identity
- Authorization
- Tool permissions
- Excessive privileges
- Memory
- Prompt injection
- Tool abuse
- Data access
- Human approval controls
Common Questions We Help Answer
What can this agent access?
What can this agent change?
What happens if the agent is manipulated?
Can an attacker use the agent's permissions against us?
Are high-risk actions properly controlled?
Learn MoreMCP Security
Secure the Tools Connecting AI to Your Systems.
The Model Context Protocol can give AI systems access to tools, services, data, and enterprise systems.
That connectivity creates new trust boundaries and security considerations.
Arapearly AI Security evaluates MCP implementations and integrations to identify weaknesses before they become attack paths.
We Assess
- MCP servers
- Tool access
- Authentication
- Authorization
- Secrets
- Data access
- Trust boundaries
- Tool invocation
- Third-party integrations
MCP Security Questions
Who can invoke each tool?
What data can each tool access?
What permissions does the AI receive?
Can an attacker manipulate tool inputs?
Can a compromised MCP server expose sensitive data?
Can tool access be abused to reach internal systems?
Learn MoreAI Application Security
Apply Application Security Principles to AI-Powered Applications.
AI applications combine traditional software with models, prompts, data, retrieval systems, APIs, and third-party services.
Arapearly evaluates the complete application rather than looking at the model in isolation.
We Assess
- LLM integrations
- Application architecture
- APIs
- Authentication
- Authorization
- Prompt handling
- Output handling
- RAG
- Vector databases
- Data access
- Third-party services
Security Risks We Look For
- Prompt injection
- Sensitive data exposure
- Insecure output handling
- Excessive permissions
- Unauthorized data access
- Insecure API integrations
- RAG data leakage
AI Supply Chain Security
Know What Your AI Depends On.
Modern AI systems depend on models, packages, datasets, containers, APIs, infrastructure, and third-party services.
Each dependency introduces potential risk.
We help organizations understand and secure their AI supply chain.
We Assess
- AI models
- Open-source packages
- Dependencies
- Containers
- Datasets
- Model provenance
- Third-party AI services
- Model repositories
Key Risks
- Malicious packages
- Compromised dependencies
- Model tampering
- Untrusted models
- Data poisoning
- Dependency vulnerabilities
AI Security Architecture
Build Security Into Your AI Architecture.
Security should be designed into AI systems before they reach production.
We help security, engineering, and technology teams design practical controls for AI applications and platforms.
Architecture Areas
- Identity and access
- AI gateways
- LLM gateways
- API security
- Agent security
- MCP security
- Data protection
- Secrets management
- Logging
- Monitoring
- Supply chain controls
We Help You Design
Secure AI architectures
AI security control frameworks
Agent permission models
MCP security boundaries
AI gateway architectures
AI application security patterns
AI supply chain controls
Learn MoreAI Threat Modeling
Understand How Your AI System Could Be Attacked.
Traditional threat modeling needs to evolve as organizations adopt AI.
We identify assets, trust boundaries, threats, attack paths, and abuse cases specific to your AI architecture.
Threat Modeling Covers
- AI applications
- Agents
- Models
- APIs
- MCP
- RAG
- Data
- Identity
- Tools
- Infrastructure
Deliverables
- Architecture data-flow diagram
- Trust boundaries
- Threat scenarios
- Abuse cases
- Attack paths
- Risk prioritization
Who We Help
Who We Help
AI security designed around your organization's needs.
Arapearly AI Security helps security, technology, and engineering leaders understand AI risk and build practical security controls across their AI environment.
CTOs & CIOs
Build security controls into AI adoption and deployment strategies.
Application Security Teams
Extend existing AppSec programs to address AI-specific threats.
AI/ML Engineering Teams
Build secure AI applications, agents, APIs, and infrastructure.
Government & Regulated Organizations
Identify and reduce AI security risks across applications, infrastructure, data, identity, and supply chains.
Our Approach
Our Approach
Discover
Understand your AI environment and business objectives.
Map
Document applications, models, agents, APIs, data, identities, and integrations.
Threat Model
Identify realistic attack paths and abuse cases.
Assess
Evaluate security controls and technical weaknesses.
Prioritize
Rank findings according to risk and business impact.
Remediate
Provide practical engineering recommendations.
Monitor
Help establish ongoing AI security practices.
Start here
Understand Your AI Security Risk Before It Becomes an Incident
The quickest way to identify the most important gaps in your AI environment is a focused security assessment.
Identify the risks.
Understand the attack paths.
Prioritize remediation.
Build a stronger AI security program.
Request an AI Security Assessment Contact Arapearly