AI Security, Governance & Compliance
Enterprise AI systems face a new category of security threats that traditional cybersecurity tools were not designed to address. We help large corporations secure their AI systems, establish governance frameworks, and meet evolving regulatory compliance requirements with practical, implementable controls.
The AI Threat Landscape Is Different
AI systems introduce attack vectors and risk categories that are fundamentally different from traditional software. Understanding these threats is the first step toward effective defense.
Prompt Injection Attacks
Adversarial inputs designed to override system instructions, extract confidential data, or manipulate model behavior. Prompt injection is the most common and most dangerous attack vector for LLM-powered applications. Direct injection embeds malicious instructions in user input, while indirect injection hides them in data the model processes from external sources. Without proper defenses, a single prompt injection can bypass access controls, leak system prompts, exfiltrate sensitive data, or cause the model to take unauthorized actions on behalf of the attacker.
Data Leakage & Exfiltration
AI systems can inadvertently expose sensitive information through their outputs — training data memorization, context window leaks, verbose error messages, and metadata exposure. Without output filtering and guardrails, LLMs may surface PII, proprietary business information, credentials, or other sensitive data that was present in their training data or provided through RAG retrieval. This risk is amplified when AI systems are connected to enterprise data sources through RAG pipelines or API integrations.
Shadow AI & Ungoverned Usage
Employees across your organization are already using AI tools — often without IT knowledge, security review, or governance oversight. Consumer AI tools, browser extensions, and third-party integrations create unmanaged data flows where sensitive corporate information is shared with external AI services. This shadow AI problem creates compliance risks, data sovereignty violations, and security exposures that grow silently until a breach or audit reveals the extent of ungoverned AI usage across the organization.
Model Poisoning & Supply Chain Attacks
Organizations increasingly rely on pre-trained models, open-source libraries, and third-party AI services. Each dependency represents a potential supply chain risk. Models can be poisoned through manipulated training data, embedding backdoors that activate under specific conditions. Open-source model weights can be tampered with. Third-party AI services can change behavior silently through model updates. Without proper supply chain security practices, organizations expose themselves to risks they cannot detect through traditional security controls.
How We Secure Enterprise AI
Our AI security services address the full spectrum of AI-specific threats with practical, implementable controls designed for enterprise environments.
AI Threat Modeling
Systematic identification and assessment of threats specific to your AI systems. We analyze your AI architecture, data flows, integration points, and attack surfaces to create a comprehensive threat model. This goes far beyond traditional application security: we evaluate prompt injection vectors, data leakage paths, model manipulation risks, and supply chain vulnerabilities specific to AI systems. The resulting threat model provides prioritized risks with recommended mitigations tailored to your architecture and risk tolerance.
Red Team Testing for AI
Adversarial testing of your AI systems by experienced AI security practitioners. We attempt prompt injection attacks, data exfiltration, system prompt extraction, guardrail bypass, and other attack techniques against your deployed AI applications. Red team results reveal vulnerabilities that design reviews and automated scanning miss. We provide detailed findings with reproduction steps, severity ratings, and specific remediation guidance. Regular red team exercises keep your defenses current as attack techniques evolve.
Guardrails & Output Filtering
Design and implementation of input validation, output filtering, and behavioral guardrails for your AI systems. We build multi-layered defense systems that validate inputs before they reach the model, constrain model behavior through system-level controls, and filter outputs before they reach users. Guardrails address PII detection and redaction, content safety, hallucination detection, topic restriction, and response quality validation. These controls operate in real-time without significantly impacting latency or user experience.
Continuous AI Monitoring
Real-time monitoring and alerting for your AI systems that goes beyond traditional application monitoring. We instrument your AI systems to track model performance, detect anomalous usage patterns, identify potential attacks, monitor output quality, and flag compliance-relevant events. Dashboards provide visibility into AI system behavior across the organization, while automated alerts surface issues that require human attention. Monitoring data also feeds continuous improvement: identifying patterns that inform guardrail updates and policy refinements.
Compliance Frameworks We Implement
AI compliance is not a checkbox exercise. We help enterprises build governance systems that satisfy regulatory requirements while remaining practical and sustainable to operate.
EU AI Act
The EU AI Act creates a risk-based regulatory framework for AI systems with significant requirements for high-risk applications. We help enterprises classify their AI systems under the Act's risk categories, implement the required risk management systems, establish the transparency and documentation requirements for high-risk systems, and prepare for conformity assessments. For organizations deploying AI that affects EU citizens, compliance is not optional — and the penalties for non-compliance are substantial. Our consultants track the Act's evolving implementation timeline and guidance.
NIST AI Risk Management Framework
The NIST AI RMF provides a structured approach to managing AI risks across the lifecycle. We help organizations implement the framework's four core functions — Govern, Map, Measure, and Manage — with practical, actionable processes rather than theoretical documentation. This includes establishing AI governance structures, mapping AI risks to organizational context, defining metrics and measurement approaches for AI trustworthiness, and implementing management processes for identified risks. The NIST AI RMF is increasingly referenced by regulators and industry standards bodies.
ISO 42001 — AI Management Systems
ISO 42001 is the international standard for AI management systems, providing a framework for responsible development, deployment, and operation of AI systems. We help organizations design and implement management systems that meet ISO 42001 requirements, including AI policy development, risk assessment processes, resource management, performance evaluation, and continuous improvement mechanisms. For organizations seeking formal certification, we guide the preparation process through gap assessment, system design, implementation support, and internal audit readiness.
Industry-Specific Compliance
AI compliance requirements vary significantly across industries. Healthcare organizations must address HIPAA considerations for AI systems processing protected health information. Financial services firms face requirements from regulators including the OCC, SEC, and FINRA around model risk management and fair lending. Government agencies must comply with executive orders and agency-specific AI policies. We help organizations understand the intersection of AI-specific regulations with existing industry compliance requirements and build unified governance frameworks that address all applicable standards.
The Shadow AI Problem
Across enterprises worldwide, employees are already using AI tools for daily work — often without the knowledge of IT, security, or compliance teams. Research consistently shows that the vast majority of enterprise employees have used generative AI tools, and a significant portion have shared sensitive corporate data with consumer AI services. This is not a technology problem; it is a governance gap.
The response cannot be simply banning AI tools — that approach fails because employees find workarounds and the usage goes further underground. Effective shadow AI governance requires a balanced approach: understanding what tools employees are using and why, assessing the actual risks those tools create, providing approved alternatives that deliver similar benefits with appropriate security controls, establishing clear and practical usage policies, implementing monitoring that provides visibility without being draconian, and building a culture where employees understand both the risks and the approved pathways for AI usage.
We help enterprises move from reactive discovery of shadow AI problems to proactive governance that channels AI enthusiasm into safe, productive, and compliant usage patterns. The goal is not to stop employees from using AI — it is to ensure they use it in ways that protect the organization while delivering the productivity benefits that AI adoption promises.
Frequently Asked Questions
Common questions about AI security, governance, and compliance for enterprise organizations.
How do you assess the security posture of our existing AI systems?+
We conduct a structured AI security assessment that evaluates your deployed AI systems across multiple dimensions: architecture review (examining data flows, integration points, and attack surfaces), threat modeling (identifying AI-specific risks like prompt injection, data leakage, and model manipulation), guardrail evaluation (testing existing input/output controls), governance review (assessing policies, processes, and oversight structures), compliance gap analysis (mapping current practices against applicable regulatory requirements), and shadow AI assessment (discovering ungoverned AI usage across the organization). The assessment produces a prioritized risk register with specific, actionable remediation recommendations.
What does AI red teaming involve and how is it different from traditional penetration testing?+
AI red teaming is adversarial testing specifically designed for AI systems. While traditional penetration testing focuses on network, application, and infrastructure vulnerabilities, AI red teaming targets AI-specific attack vectors: prompt injection (both direct and indirect), system prompt extraction, guardrail bypass techniques, data exfiltration through conversational manipulation, model behavior manipulation, and abuse of tool-calling or function-calling capabilities. Our red team practitioners combine deep understanding of LLM behavior with security expertise to identify vulnerabilities that standard security tools and processes cannot detect. We test in a controlled manner that provides comprehensive coverage without disrupting production systems.
How do you address the shadow AI problem across a large organization?+
Shadow AI — unauthorized use of AI tools by employees — requires a combination of technical controls and organizational change, not just policy enforcement. We help organizations with discovery (identifying what AI tools employees are actually using and what data is being shared), risk assessment (evaluating the security and compliance implications of discovered shadow AI usage), policy development (creating clear, practical AI usage policies that employees can actually follow), sanctioned alternatives (implementing approved AI tools that meet security requirements while delivering the productivity benefits employees are seeking), technical controls (network-level monitoring and controls for AI service access), and education (training programs that help employees understand both the risks and the approved pathways for AI usage).
Do we need AI governance if we are only using third-party AI APIs?+
Yes. In many ways, third-party AI API usage creates more governance needs, not fewer. When you use external AI services, you are sending enterprise data to third-party infrastructure, relying on models you do not control or audit, subject to terms of service and privacy policies that can change, unable to guarantee data handling practices, and potentially creating compliance exposures for regulated data. AI governance for API-based AI usage includes vendor assessment and due diligence, data classification and handling policies (what data can and cannot be sent to external AI services), acceptable use policies, monitoring and audit logging, incident response procedures, and contractual protections. This governance is essential whether you are running models internally or consuming them as services.
How do you keep AI security and governance current as regulations evolve?+
AI regulation and security best practices are evolving rapidly. We help organizations build governance frameworks that are designed for adaptability rather than static compliance with today's requirements. This includes establishing governance structures with clear ownership and review cadences, implementing monitoring that tracks regulatory developments relevant to your industry and jurisdictions, building modular policy frameworks that can be updated as requirements change, and maintaining ongoing advisory relationships that keep your governance current. For managed service clients, we provide regular regulatory updates, policy revision recommendations, and proactive assessment of how new requirements affect your AI systems and practices.