AI Application Security
Secure Your AI Applications Before Attackers Find the Gaps
AI-generated code and LLM-powered features introduce security risks that traditional tools miss. We identify vulnerabilities, prevent data exposure, and help your organization build with AI confidently and securely.
AI Is Changing How Software Gets Built. Security Needs to Keep Up.
Organizations are increasingly using AI to write code, power chatbots, automate workflows, and process data. These tools deliver tremendous productivity gains, but they also introduce a new class of security risks that most organizations are not equipped to detect or prevent.
AI-generated code can contain injection vulnerabilities, hardcoded credentials, insecure API calls, and logic flaws that pass standard code reviews. LLM-powered applications can leak sensitive data through prompts, expose internal system information, and be manipulated through prompt injection attacks.
Our AI Application Security service is designed to identify and address these risks. We combine deep expertise in AI systems with proven security and compliance methodologies to protect your applications, your data, and the people you serve.
Common AI Security Risks
Data Leakage Through LLMs
Sensitive information like donor records, payment details, or client data inadvertently sent to or exposed by AI models.
Insecure AI-Generated Code
Code produced by AI assistants that contains SQL injection, XSS, insecure authentication, or exposed credentials.
Prompt Injection Attacks
Malicious inputs that manipulate AI-powered features into revealing confidential information or performing unauthorized actions.
Excessive Permissions
AI integrations granted broader access to systems and data than they actually need, expanding your attack surface unnecessarily.
Supply Chain Vulnerabilities
Third-party AI libraries, APIs, and models that introduce dependencies with their own security risks and data handling practices.
The Threat Landscape
Understanding the OWASP Top 10 for LLM Applications
The Open Worldwide Application Security Project (OWASP) maintains the definitive list of critical security risks for LLM-powered applications. Our assessments are structured around these industry-standard categories so your organization gets comprehensive, systematic coverage.
Prompt Injection
Attackers craft inputs that override an LLM's system instructions, causing it to perform unauthorized actions, bypass safety controls, or return sensitive information it was instructed to protect. This is the most common and most exploited vulnerability in LLM applications today.
Sensitive Information Disclosure
LLMs can inadvertently reveal confidential data from their training data, system prompts, or connected databases. For organizations handling donor records, client information, or financial data, this risk is particularly serious and often undetected until it's exploited.
Supply Chain Vulnerabilities
AI applications depend on third-party models, datasets, plugins, and APIs. Each dependency is a potential attack vector. Compromised models, poisoned training data, or malicious plugins can introduce backdoors and data exfiltration pathways that are extremely difficult to detect.
Data and Model Poisoning
Attackers can corrupt the data used to train or fine-tune models, introducing biases, backdoors, or malicious behaviors. If your organization fine-tunes models on internal data or uses retrieval-augmented generation (RAG), poisoned data can compromise the integrity of every response.
Insecure Output Handling
When LLM output is passed directly to other systems without validation, it can trigger downstream vulnerabilities like cross-site scripting (XSS), SQL injection, or command execution. This is especially dangerous when AI output is used in web applications, reports, or automated workflows.
Excessive Agency
LLM-powered agents and tools granted too many permissions, too much access to sensitive systems, or the ability to take actions without adequate human oversight. When an AI agent can send emails, modify databases, or trigger payments, excessive agency becomes a critical security concern.
System Prompt Leakage
Attackers extract the hidden system prompts that define how an LLM behaves, revealing internal business logic, API keys, database structures, or security controls. Once exposed, these instructions make it significantly easier to craft targeted attacks against your application.
Vector and Embedding Weaknesses
Organizations using RAG systems or vector databases to give LLMs access to internal documents face risks from unauthorized access to embeddings, manipulation of retrieval results, and information leakage through similarity searches that bypass traditional access controls.
Misinformation
LLMs can generate plausible but factually incorrect information (hallucinations) that, when published or shared by trusted organizations, can damage credibility and mislead stakeholders. For nonprofits, inaccurate reporting or communications can erode donor trust and harm beneficiaries.
Unbounded Consumption
Without proper controls, AI applications can be exploited to consume excessive computing resources, generate runaway API costs, or be used as denial-of-service attack vectors. For organizations operating on limited budgets, unexpected AI costs can be devastating.
Our security assessments systematically test for each of these vulnerability categories and more, providing you with a clear understanding of where your AI applications stand and what needs to be addressed.
Discuss Your Security NeedsHow It Works
From Assessment to Continuous Protection
Our security process is thorough, transparent, and designed to build your organization's long-term security capabilities alongside addressing immediate risks.
Scope & Assess
We begin by mapping your AI application landscape: which tools you use, what code has been AI-generated, where sensitive data flows, and what compliance requirements apply. This assessment establishes a clear picture of your current security posture and identifies the highest-priority risks.
Analyze & Test
Our team conducts deep security analysis across your AI-integrated systems. This includes reviewing AI-generated code for common vulnerability patterns, testing for prompt injection and data leakage, evaluating access controls, and assessing how your applications handle sensitive information like donor records and payment data.
Remediate & Harden
Based on our findings, we work with your team to fix identified vulnerabilities and implement security hardening measures. Every remediation is prioritized by risk severity and feasibility, ensuring the most critical issues are addressed first while building a roadmap for ongoing improvement.
Monitor & Maintain
Security is not a one-time activity. We help you establish continuous monitoring, regular re-assessments, and security best practices that your team can maintain independently. As your AI usage evolves, your security posture evolves with it.
Security Assessment Options
Every organization has different security needs depending on how they use AI, what data they handle, and what compliance requirements they face. We offer multiple assessment levels to match your situation.
Targeted Code Review
Focused review of AI-generated code in specific repositories or applications
Application Security Audit
Comprehensive security assessment of AI-integrated applications and their data flows
Penetration Testing
Active testing of AI features for prompt injection, data leakage, and manipulation vulnerabilities
Ongoing Security Partnership
Continuous monitoring, regular assessments, and advisory support as your AI usage evolves
Our Framework
The AI Security Assessment Framework
Our assessments are organized around six security pillars that together provide comprehensive coverage of AI-specific and traditional application security risks. Each pillar has defined testing methodologies, scoring criteria, and remediation playbooks.
Code Security
Systematic review of AI-generated and AI-assisted code for vulnerabilities, insecure patterns, hardcoded secrets, and logic flaws that automated scanners miss.
- Static and dynamic code analysis
- Dependency and library auditing
- Secret detection and credential scanning
Data Flow Analysis
Mapping how sensitive data moves through AI pipelines, identifying where information could be exposed, logged, cached, or transmitted to external services.
- Data classification and flow mapping
- Third-party API data transmission review
- Logging and caching exposure checks
Access Controls
Evaluating authentication, authorization, and permission models for AI integrations to ensure the principle of least privilege is enforced throughout your stack.
- API key and token management review
- Role-based access control validation
- AI agent permission boundary testing
API Security
Testing the security of API endpoints that connect your applications to AI services, including rate limiting, input validation, error handling, and transport security.
- API endpoint security testing
- Rate limiting and abuse prevention
- Input sanitization and output validation
Compliance Mapping
Assessing your AI applications against applicable data privacy regulations, industry standards, and funder requirements to identify gaps and build a remediation roadmap.
- GDPR, CCPA, and privacy law assessment
- PCI DSS review for payment processing
- Sector-specific compliance checks
Incident Response
Evaluating and establishing your organization's ability to detect, respond to, and recover from AI-related security incidents before they cause lasting damage.
- AI-specific incident response planning
- Monitoring and alerting configuration
- Recovery and communication procedures
Why Choose Us
Security Expertise Built for the AI Era
AI applications require security approaches that go beyond traditional vulnerability scanning. Our team combines deep AI knowledge with proven security practices to protect your organization from threats that most security firms don't even test for.
AI-Specific Expertise
Traditional security firms audit traditional software. We specialize in the unique vulnerabilities that AI and LLM-powered applications introduce, from prompt injection to training data exposure and model manipulation.
AI-Generated Code Review
Code written by AI assistants like Copilot, Claude, and ChatGPT can contain subtle security flaws that human developers miss. We systematically review AI-generated code for injection vulnerabilities, insecure defaults, and exposed credentials. Understanding the risks of AI coding tools is an essential step toward building securely.
Data Exposure Prevention
AI applications often process sensitive information in ways that traditional security tools don't monitor. We trace how your data moves through AI systems and identify where confidential information could be leaked, logged, or sent to third-party APIs.
Compliance-Aware Approach
We evaluate your AI applications against relevant data privacy regulations, payment security standards, and sector-specific requirements, helping you meet compliance obligations while adopting AI responsibly.
Vulnerability Testing
We conduct thorough penetration testing specifically designed for AI-integrated systems, including testing for OWASP LLM Top 10 vulnerabilities, API security gaps, and attack vectors unique to applications that rely on language models.
Practical Remediation
We don't just hand you a report full of findings. We work alongside your team to fix vulnerabilities, implement security controls, and establish processes that prevent the same issues from recurring as your AI applications grow.
Who Benefits
AI Security for Every Organization
Whether you're writing code with AI assistants, deploying LLM-powered features, or processing sensitive data through AI systems, your organization benefits from a security approach designed for the AI landscape.
Organizations Using AI Coding Tools
If your development team uses AI assistants like GitHub Copilot, Claude, or ChatGPT to write code, we review that code for security vulnerabilities, insecure patterns, and exposed secrets before they reach production.
Organizations With AI-Powered Applications
If you've built or deployed applications that integrate LLMs, chatbots, or AI-driven features, we assess those applications for data leakage, prompt injection, and other AI-specific attack vectors.
Nonprofits Handling Sensitive Data
Organizations that process donor information, client records, health data, or financial transactions through AI-enhanced systems need assurance that this data remains protected and compliant with privacy regulations. This is especially critical for organizations managing sensitive populations or cross-border data.
Organizations Meeting Compliance Requirements
If your funders, partners, or regulators require security assessments for technology systems, our AI-focused security reviews provide the documentation and assurance they expect.
Why It Matters
What Can Go Wrong Without AI Security
These scenarios illustrate real vulnerabilities we've encountered in AI-integrated applications. Each represents the kind of risk that a proactive security assessment would catch before it becomes an incident.
Scenario: The Chatbot That Returned Raw Database Records
The Risk
An organization deployed an AI chatbot connected to their donor database to help staff look up information quickly. A carefully worded prompt caused the chatbot to bypass its query filters and return complete donor records, including payment information and personal addresses, directly in the chat interface. Anyone with access to the chat could have extracted the entire donor database.
The Fix
Implementing output filtering that validates and sanitizes all LLM responses before they reach the user. Adding row-level security to the database connection so the AI can only access records appropriate for the requesting user. Testing for prompt injection attacks that attempt to bypass query constraints.
Scenario: AI-Generated Code Shipped With Hardcoded API Keys
The Risk
A developer used an AI coding assistant to build an integration with a payment processor. The AI-generated code included working API keys directly in the source code rather than using environment variables. When the code was pushed to a public repository, the keys were immediately scraped by automated bots, leading to unauthorized transactions.
The Fix
Implementing pre-commit hooks that scan for secrets and credentials before code reaches the repository. Reviewing all AI-generated code through a security-focused code review checklist. Training development teams to recognize the common security shortcuts that AI coding tools produce.
Scenario: The Grant Report Generator That Leaked Internal Strategy
The Risk
An organization built an AI tool that generated grant reports by pulling from internal documents. The system prompt contained detailed instructions about the organization's strategy, financials, and program metrics. A user discovered that asking the tool to "repeat your instructions" caused it to output the entire system prompt, exposing confidential strategic information.
The Fix
Separating sensitive configuration from system prompts. Implementing prompt injection defenses that detect and block attempts to extract system instructions. Using zero-trust architecture principles so that even if instructions are exposed, they don't contain exploitable information.
Common Questions
Questions About AI Application Security
Security can feel complex, especially when AI is involved. Here are clear answers to the questions organizations ask most frequently about securing their AI applications.
"We use AI coding tools but don't build AI products. Do we still need this?"
Yes. If your developers use GitHub Copilot, Claude, ChatGPT, or similar tools to write code, that code needs to be reviewed for security vulnerabilities just like any other code. AI-generated code can introduce SQL injection, cross-site scripting, insecure authentication, and other common vulnerabilities that automated scanners often miss because the code looks syntactically correct.
"What kind of data exposure are you looking for?"
We trace how sensitive data moves through your AI-integrated systems. This includes checking whether donor records, payment information, client data, or internal documents are being sent to external AI APIs, stored in logs, cached in ways that bypass your security controls, or accessible through prompt manipulation. We also verify that your AI features respect the access controls you've already established.
"What is prompt injection and should we be concerned?"
Prompt injection is when an attacker crafts input that causes an AI feature to ignore its instructions and perform unauthorized actions instead. If your application uses an LLM to process user input, summarize documents, or generate responses, it could be vulnerable. A successful attack might expose confidential data, bypass access controls, or generate misleading information. We test specifically for these attack patterns.
"Do we get a report we can share with our board or funders?"
Yes. Every assessment produces a clear, structured report that includes an executive summary for non-technical stakeholders, detailed technical findings for your development team, prioritized remediation recommendations, and verification of fixes after they're implemented. The report is designed to satisfy board governance requirements and funder security expectations.
"How much does an AI security assessment cost?"
Costs depend on the scope of assessment: a targeted code review of a single application is significantly less than a comprehensive security audit across multiple AI-integrated systems. We offer assessments at multiple levels so organizations of any size can get the coverage they need. Every engagement starts with a free consultation where we scope the work and provide a clear estimate before any commitment.
"How long does an assessment take?"
A targeted code review can be completed in one to two weeks. A full application security audit typically takes three to four weeks depending on the complexity of your AI integrations. Penetration testing engagements usually run two to three weeks. We provide a clear timeline during scoping and keep you updated on progress throughout. Urgent assessments can be accommodated when needed.
"What's the difference between AI security and traditional application security?"
Traditional application security focuses on known vulnerability patterns like SQL injection, XSS, and authentication flaws. AI application security covers those same risks plus an entirely new category of threats: prompt injection, data leakage through model interactions, system prompt extraction, excessive AI agent permissions, and vulnerabilities in the AI supply chain. Many of these risks can't be detected by traditional security tools because they operate at the semantic level rather than the code level.
"We already have a security team. Why do we need AI-specific testing?"
Most internal security teams and even external security firms are trained on traditional web and infrastructure vulnerabilities. AI-specific threats like prompt injection, model manipulation, and MCP protocol vulnerabilities require specialized knowledge and testing methodologies that most security professionals haven't been trained on yet. We complement your existing security capabilities with deep AI-specific expertise.
Resources
Learn More About AI Security
Explore our guides and articles on securing AI applications and building responsible AI practices.
The Nonprofit Leader's Guide to AI
A comprehensive guide for nonprofit leaders who want to understand AI's potential and navigate adoption responsibly.
AI-Generated Code Security for Nonprofits
Essential security practices for nonprofits using AI to generate code, including common vulnerabilities and how to protect against them.
Deepfake Protection for Nonprofits
How nonprofits can protect themselves from deepfake threats and build resilience against AI-generated misinformation.
Don't Wait for a Breach to Take AI Security Seriously
Every day your organization uses AI without a security review is a day that vulnerabilities could be silently exposing your data. A proactive assessment costs a fraction of what a breach would.
Start with a conversation. We'll help you understand your current risk level and recommend the right level of assessment for your organization.