Who is an AI Security Audit Engineer?
Tom Smykowski is a product-minded staff software engineer with nearly two decades of experience in building and securing complex software systems. As an AI security audit engineer, he specializes in evaluating the security posture of AI-powered applications, from foundational model integrations to full-stack AI product architectures. His expertise is best suited for founders, CTOs, and executives who need ownership from discovery through production, ensuring their AI initiatives are not only innovative but also secure, compliant, and built to scale. Hiring Tom means bringing on a seasoned engineer who combines deep technical scrutiny with product and UX thinking to identify and mitigate risks before they impact your users or your business.
The role of an AI security audit engineer extends far beyond traditional code review. It encompasses the entire AI software development lifecycle, including data pipeline security, model provenance, prompt injection vulnerabilities, output validation, and the security implications of AI agents and autonomous workflows. In an era where AI features are rapidly deployed, a dedicated audit is critical to prevent data leaks, ensure regulatory compliance, and maintain user trust. Tom’s approach is holistic, examining not just the code but the design decisions, third-party dependencies, and operational practices that surround your AI implementation.
Operating remotely from Poland, Tom seamlessly integrates with international teams across the EU, UK, and US time zones. His background includes staff engineering roles at scale-up companies and hands-on founding experience, giving him a pragmatic understanding of both startup agility and enterprise-grade security requirements. Whether you’re launching a new AI feature or need to harden an existing system, an AI security audit provides the confidence to move forward securely.
Core Components of an AI Security Audit
A comprehensive AI security audit involves several key pillars. First is Architecture and Design Review. This examines the overall system design for potential security flaws. How are AI models integrated? Where is sensitive data processed? Tom evaluates the separation of concerns, API security, and the trust boundaries between your application, AI services, and data stores. He draws on experience from projects like the Printify design system rebrand, which supported over 10 million merchants, to assess scalability and security in tandem.
The second pillar is Code and Implementation Analysis. This involves a deep dive into the source code, focusing on areas where AI logic interacts with application logic. Tom looks for common vulnerabilities such as improper input sanitization, insecure direct object references in AI context management, and flaws in how AI outputs are parsed and acted upon. His technical stack proficiency in Vue.js, TypeScript, and full-stack development allows him to audit both frontend and backend components effectively.
Finally, an audit must cover Compliance and Data Governance. This is especially crucial for teams in regulated industries or handling personal data. The audit verifies adherence to standards like GDPR, checks data lineage for AI training sets, and ensures audit trails are in place. Tom’s work at Croissant, which involved compliance and collaboration features for a workplace platform, gives him direct experience in building systems that meet stringent regulatory requirements while remaining user-friendly.
Why Security Audits Are Critical for AI Products
AI products introduce unique and amplified security risks. Traditional web vulnerabilities persist, but new threat vectors emerge specifically from AI capabilities. For instance, prompt injection attacks can manipulate an LLM into revealing system instructions, accessing unauthorized data, or performing unintended actions. A security audit identifies if your system is vulnerable to such attacks and recommends mitigation strategies, such as rigorous input validation and output filtering.
Another critical area is model security and supply chain integrity. An audit examines the provenance of the AI models you use, whether open-source or proprietary. It assesses the risks of model poisoning, checks for proper versioning and validation of model weights, and evaluates the security of the inference endpoints. For companies leveraging multiple AI services, the audit maps the entire supply chain to identify single points of failure or compromise.
Furthermore, AI can exacerbate existing issues like data leakage. User inputs sent to an AI service might contain sensitive information. An audit reviews data handling practices, encryption in transit and at rest, and data retention policies specific to AI interactions. The business impact of a breach in an AI system can be catastrophic, not just in data loss but in erosion of user trust and potential regulatory fines. Proactive auditing is a strategic investment in product longevity and brand reputation.
Tom’s Methodology for AI Security Audits
Tom employs a structured, multi-phase methodology tailored to AI systems. The process begins with a Scoping and Discovery Workshop. This collaborative session with your technical leadership defines the audit’s boundaries, priorities, and success criteria. It ensures the audit focuses on the most critical components of your AI application, whether it’s a customer-facing chatbot, an internal coding agent, or a data analysis pipeline.
The next phase is the Technical Deep Dive and Analysis. Using a combination of automated scanning tools and manual code review, Tom examines the codebase, infrastructure configuration, and AI workflow design. He looks for security anti-patterns, tests for common vulnerabilities, and analyzes the system’s behavior under edge cases. His experience in establishing AI quality gates and AI-assisted code review processes informs this phase, ensuring checks are both rigorous and pragmatic for engineering teams.
The audit culminates in a Detailed Reporting and Remediation Planning phase. Tom delivers a clear, actionable report that categorizes findings by severity (critical, high, medium, low). Each finding includes a description of the vulnerability, its potential impact, and concrete remediation steps. More than just a list of problems, the report serves as a roadmap for your engineering team to improve security posture. For ongoing support, Tom can also provide advisory on implementing the fixes and integrating security checks into your CI/CD pipeline.
Integrating Security into Your AI Development Lifecycle
An audit is not a one-time event but a catalyst for building security into your culture. Tom advocates for shifting security left in the AI development process. This means incorporating security considerations during the design and prototyping stages, not just as a final pre-launch check. Teams can adopt practices like threat modeling for new AI features and security-focused user stories during sprint planning.
Tooling and automation play a key role. An audit often recommends integrating AI-specific security scanners and linters into your development environment and CI/CD pipeline. These tools can automatically detect issues like hardcoded API keys for AI services, overly permissive model prompts, or insecure data serialization patterns. Tom’s expertise in AI software factory setup, including the use of AI coding agents and automation, helps teams implement these guardrails without sacrificing development velocity.
Finally, fostering a security-aware team culture is essential. Tom supports this through follow-up workshops and training sessions. He can conduct corporate AI training focused on secure coding practices for AI, or executive briefings for leadership on AI risk management. His background in technical writing, public speaking, and mentoring enables him to effectively communicate complex security concepts to engineers, product managers, and executives alike, ensuring everyone is aligned on security priorities.
When This is a Good Fit
Engaging Tom for an AI security audit is an ideal fit if you are a founder, CTO, or technical lead overseeing an AI product or feature and need an independent, expert assessment of its security. It is particularly valuable before a major launch, after a significant architecture change, or when preparing for compliance reviews or funding rounds. If your team is building with AI but lacks dedicated security expertise, or if you simply want a seasoned second opinion to validate your security measures, this service provides the necessary assurance. It is designed for teams who value robust, production-ready software and want to proactively address the unique security challenges posed by modern AI integration.