AI Security & Compliance — Defend the New Attack Surface

AI systems introduce entirely new attack surfaces that traditional cybersecurity tools and processes were never designed to address. Prompt injection can hijack LLM behaviour to bypass safety restrictions and extract confidential system prompts. Data poisoning corrupts training pipelines, embedding backdoors that activate on specific triggers. Model extraction attacks steal proprietary intellectual property by systematically querying APIs. Sensitive data leaks through model outputs when PII from training data surfaces in responses. The OWASP LLM Top 10 documents these risks, but most security teams — even those with strong cloud security services — lack the AI-specific expertise to assess, prioritise, and mitigate them effectively. Opsio secures AI systems at every layer with defense-in-depth architecture: input validation and sanitization against both direct and indirect prompt injection attacks, output filtering for PII and sensitive data leakage, model API access controls with authentication and rate limiting, adversarial robustness testing against evasion and poisoning, supply chain security for ML dependencies and pre-trained model weights, and compliance controls mapped to GDPR, EU AI Act, OWASP LLM Top 10, and NIST AI Risk Management Framework. We protect Claude, GPT-4, Gemini, and self-hosted open-source deployments with equal rigour.

The fundamental challenge of AI security is balancing protection with usefulness. Overly restrictive guardrails make AI systems useless — blocking legitimate queries, refusing valid requests, and frustrating users until they find workarounds that bypass security entirely. Opsio's approach implements proportionate controls that protect against genuine threats without destroying the business value your AI systems were built to deliver. We tune guardrails to your specific risk profile, use case requirements, and regulatory obligations.

For LLM deployments specifically, we implement production guardrails covering the complete OWASP LLM Top 10 attack taxonomy: prompt injection (LLM01), insecure output handling (LLM02), training data poisoning (LLM03), model denial of service (LLM04), supply chain vulnerabilities (LLM05), sensitive information disclosure (LLM06), insecure plugin design (LLM07), excessive agency (LLM08), overreliance (LLM09), and model theft (LLM10). Each risk gets specific, testable controls with monitoring and alerting that operate continuously in production.

Common AI security gaps we discover during assessments: LLM applications with no input validation — allowing trivial prompt injection, model APIs exposed without authentication or rate limiting, training pipelines pulling unverified pre-trained weights from public repositories, conversation logs stored indefinitely with PII in plaintext, no incident response playbook for AI-specific security events, and third-party AI tools integrated without security evaluation. These gaps exist because traditional security teams don't know what to look for in AI systems. Opsio's AI security assessment catches every one of them.

Our AI red teaming goes beyond automated scanning to simulate real-world adversarial attacks against your AI systems. Experienced AI red teamers conduct prompt injection campaigns across multiple attack vectors, jailbreak attempts using published and novel techniques, data extraction probes targeting training data and system prompts, privilege escalation through tool use and function calling, social engineering via AI personas, and denial-of-service attacks targeting model inference infrastructure. The result is a detailed findings report with severity ratings, exploitation evidence, and prioritised remediation steps. Wondering whether your AI systems are vulnerable or how AI security compares to your existing security programme maturity? Our threat assessment provides a clear picture — with actionable recommendations prioritised by risk and effort.