Traditional cybersecurity protects infrastructure, networks, and applications using firewalls, endpoint detection, and signature-based defenses. AI security addresses threats that bypass these controls entirely. Prompt injection manipulates model behavior through natural language inputs that appear legitimate. Adversarial machine learning uses carefully crafted inputs to evade detection. Data leakage occurs when models memorize training data. Model poisoning corrupts training datasets to introduce backdoors. These attacks require AI-native defenses: input validation designed for ML systems, output filtering that understands model behavior, adversarial testing, and monitoring for model-specific anomalies. Organizations need both traditional cybersecurity and AI-specific security in 2026.

Prompt injection is an attack where malicious instructions are embedded in user input (direct injection) or external data sources like documents or web pages (indirect injection) to manipulate LLM behavior. Attackers use it to bypass content filters, exfiltrate sensitive data, execute unauthorized tool actions, or change the model's behavior entirely.

Defense requires multiple layers: input sanitization that removes or escapes injection attempts, prompt templating with variable escaping, output filtering to catch policy violations, context isolation to prevent cross-session contamination, guardrail enforcement through separate validation models, and monitoring for suspicious patterns. No single control provides complete protection. Defense-in-depth is essential.

Yes. AI models can memorize and regurgitate sensitive training data including PII, trade secrets, and classified information. Membership inference attacks can determine whether specific data was used in training. Model inversion attacks can reconstruct training examples. Prompt injection can trick models into revealing data they should protect.

Prevention requires controls at every stage. During training: differential privacy to limit memorization, dataset sanitization to remove sensitive information, and parameter configuration to reduce overfitting. During deployment: output filtering and redaction, context isolation, query limiting to prevent extraction attacks, and monitoring for suspicious inference patterns. Our strategy integrates with GDPR, HIPAA, and CMMC compliance requirements.

Model poisoning occurs when attackers corrupt training data or fine-tuning datasets to introduce backdoors, bias, or degraded performance. Poisoned models behave normally under most conditions but exhibit malicious behavior when specific triggers are present. Poisoning risks are especially high when using external data sources, crowdsourced labeling, or pre-trained models from public repositories.

We defend through data provenance tracking and validation, statistical analysis to detect outliers in training data, behavioral testing to identify backdoors before deployment, model validation against clean baselines, and continuous monitoring for drift or behavioral changes. For organizations training models, we secure MLOps pipelines with access controls, audit logging, and reproducible builds.

Yes. Third-party AI providers secure their own infrastructure, but they cannot protect against attacks targeting your application layer, business logic, or data handling. Prompt injection, data leakage through API requests, insecure integration, and inadequate access controls remain your responsibility under the shared responsibility model.

You must implement input validation before API calls, output filtering after responses, rate limiting and abuse detection, API key rotation and secrets management, logging and monitoring of usage, data governance for information sent to external APIs, and guardrails enforcing your business policies. We help organizations securely integrate third-party AI services through defense-in-depth controls and compliance management.

AI introduces complex compliance challenges. GDPR requires explainability, data minimization, and the right to deletion. HIPAA demands confidentiality controls for protected health information processed by AI. CMMC requires controlled unclassified information protection. SOC 2 requires security controls and audit trails. Emerging AI-specific regulations add requirements for bias testing, transparency, and human oversight.

We help through gap assessments, control implementation (differential privacy, audit logging, explainability frameworks), policy development for AI governance, documentation for audits, and ongoing compliance monitoring. Our team holds CMMC Certified Registered Practitioner certification. Learn more on our AI compliance page.

We provide AI-specific incident response led by Craig Petronella, Licensed Digital Forensic Examiner. Our process includes immediate containment, forensic analysis of attack vectors, evidence collection, model rollback or patching, recovery procedures, and post-incident remediation with root cause analysis.

AI incident investigation requires specialized expertise. We analyze model behavior to identify anomalies, reconstruct attack sequences through log analysis, determine data exposure, and identify systemic weaknesses. We establish tailored AI incident response playbooks and offer 24/7 coverage through our managed security services.

We conduct AI red teaming that combines traditional penetration testing with AI-specific attack techniques. For LLM applications: prompt injection, jailbreaking, tool use manipulation, and RAG poisoning. For ML models: adversarial examples using FGSM, PGD, C&W, and other methods. For computer vision: physical-world adversarial patches. We also test model extraction, membership inference, and supply chain integrity.

Testing occurs in controlled environments mirroring production. We document every successful exploit with proof-of-concept, CVSS scoring, and remediation recommendations. After fixes, we retest to validate effectiveness. Our methodology incorporates the OWASP Top 10 for LLM Applications and MITRE ATLAS framework to ensure comprehensive coverage of both known and emerging attack vectors.