AI Security & Compliance — Defend the New Attack Surface
Traditional cybersecurity doesn't cover AI-specific threats. Prompt injection hijacks LLM behaviour, data poisoning corrupts models, and PII leaks through outputs. Opsio secures your AI systems with defense-in-depth controls — from input validation to red teaming — mapped to OWASP LLM Top 10.
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OWASP
LLM Top 10
100%
Coverage
Red Team
Validated
<24h
Incident Response
What's Included
Prompt Injection Protection
Multi-layer defense against prompt injection: input sanitisation and pattern detection, system prompt isolation and hardening, output validation against injection artifacts, and behavioural monitoring for anomalous model responses. We protect against both direct injection (malicious user input) and indirect injection (poisoned data sources) documented in OWASP LLM01.
LLM Data Privacy Controls
PII detection and masking in both inputs and outputs using named entity recognition and pattern matching, data residency enforcement for model API interactions, configurable conversation data retention policies, and privacy-preserving inference techniques. Ensure every LLM deployment complies with GDPR data minimisation and purpose limitation requirements.
Model Governance & Access Control
Authentication, authorisation, and rate limiting for AI model APIs with zero-trust principles. Comprehensive audit logging of all model interactions with tamper-evident storage, version control for deployed models with rollback capability, and approval workflows for model updates — establishing the accountability and traceability that regulators and auditors expect.
Adversarial Robustness Testing
Systematic testing against adversarial examples, edge cases, evasion techniques, and poisoning scenarios. We evaluate model behaviour under adversarial conditions including input perturbation, gradient-based attacks, data poisoning, and model extraction attempts — identifying vulnerabilities before real attackers exploit them in production.
OWASP LLM Top 10 Controls
Structured mitigation of all ten OWASP LLM risks with specific, testable controls for each: prompt injection defenses, output sanitisation, training pipeline integrity verification, inference rate limiting, dependency scanning, data leakage prevention, plugin sandboxing, agency constraints, confidence calibration, and model access protection.
AI Red Teaming
Adversarial security testing by experienced AI red teamers: prompt injection campaigns across multiple vectors, jailbreak attempts using published and novel techniques, data extraction probes targeting system prompts and training data, privilege escalation through tool use, and social engineering via AI personas. Detailed findings report with exploitation evidence and remediation priorities.
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Our AWS migration has been a journey that started many years ago, resulting in the consolidation of all our products and services in the cloud. Opsio, our AWS Migration Partner, has been instrumental in helping us assess, mobilize, and migrate to the platform, and we're incredibly grateful for their support at every step.
Roxana Diaconescu
CTO · SilverRail Technologies
AI Security for the LLM Era
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. Where you would rather consume these controls than build them, our managed private AI gateway applies secret blocking, PII pseudonymisation and EU-only model routing to every request.
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. Featured reading from our knowledge base: NIS2 and OT Security: Compliance Guide for Industry, Top 10 AWS Cloud Security Tools for Identity and Compliance – Opsio, and AWS Security Consultant: Mastering Cloud Security Architecture and Compliance. Related Opsio services: AI Governance Consulting — Compliance Without Paralysis.
How Opsio Compares
| Capability | DIY / Traditional Security | Generic AI Vendor | Opsio AI Security |
|---|---|---|---|
| Prompt injection defense | None (not detected) | Basic input filter | Multi-layer defense + monitoring |
| OWASP LLM Top 10 coverage | 0-2 risks addressed | 3-5 risks addressed | All 10 risks with testable controls |
| Red teaming | Traditional pen test only | Automated scanning | Expert AI red team + manual testing |
| PII protection | Network-level only | Basic output filter | Input + output masking + residency |
| Model governance | None | Basic API logging | Full audit trail + approval workflows |
| Incident response | Generic IR playbook | AI vendor support | AI-specific IR with <24h response |
| Typical annual cost | $40K+ (gaps remain) | $60-100K (partial coverage) | $102-209K (comprehensive) |
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What You Get
Pricing & Investment Tiers
Transparent pricing. No hidden fees. Scope-based quotes.
AI Threat Assessment
$15,000–$30,000
1-2 week engagement
Security Implementation
$30,000–$65,000
Most popular — full hardening
Continuous AI Security
$6,000–$12,000/mo
Ongoing monitoring
Transparent pricing. No hidden fees. Scope-based quotes.
Questions about pricing? Let's discuss your specific requirements.
Why Choose Opsio for Cloud Services
OWASP LLM Top 10 specialists
Complete mitigation coverage across all ten LLM security risk categories with testable production controls.
Multi-platform expertise
Security hardening for Claude, GPT-4, Gemini, Llama, Mistral, and custom self-hosted deployments.
Privacy by design
PII masking, data residency enforcement, and retention controls baked into every AI deployment layer.
Red team validated
Every security implementation tested by adversarial AI red teamers before sign-off — not just reviewed.
Regulatory compliance mapped
Controls explicitly mapped to EU AI Act, GDPR, NIST AI RMF, SOC 2, and ISO 27001 requirements.
Security without crippling utility
Proportionate guardrails that protect against real threats without destroying AI business value.
Not sure yet? Start with a pilot.
Begin with a focused 2-week assessment. See real results before committing to a full engagement. If you proceed, the pilot cost is credited toward your project.
Our 4-Phase Delivery Process
Threat Assessment
Evaluate all AI systems for security risks, privacy gaps, OWASP LLM Top 10 exposure, and regulatory compliance requirements. Deliverable: AI threat model with prioritised vulnerability findings. Timeline: 1-2 weeks.
Control Architecture
Design layered security controls: input validation, output filtering, access controls, monitoring, audit logging, and incident response procedures — proportionate to your risk profile and regulatory obligations. Timeline: 2-3 weeks.
Implementation & Hardening
Deploy guardrails, privacy controls, governance workflows, monitoring dashboards, and automated alerting across all AI systems. Integrate with existing SIEM and security operations infrastructure. Timeline: 3-5 weeks.
Red Teaming & Validation
Adversarial testing by experienced AI red teamers to validate controls, identify residual vulnerabilities, and verify detection and response capabilities under realistic attack conditions. Remediation cycle included. Timeline: 2-3 weeks.
Key Takeaways
- Prompt Injection Protection
- LLM Data Privacy Controls
- Model Governance & Access Control
- Adversarial Robustness Testing
- OWASP LLM Top 10 Controls
Industries Served by Opsio
Enterprise AI
Securing customer-facing chatbots, internal copilots, and AI-powered decision systems.
Healthcare
Clinical AI security, patient data protection, and HIPAA-compliant AI deployments.
Financial Services
Trading algorithm security, fraud detection AI integrity, and regulatory AI compliance.
Government & Defence
Public service AI security, transparency requirements, and sovereign AI deployment controls.
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AI Security & Compliance — Defend the New Attack Surface — FAQ
What is prompt injection and why is it the top AI security risk?
Prompt injection is an attack where malicious input manipulates LLM behaviour — bypassing safety restrictions, extracting confidential system prompts, exfiltrating sensitive data, or causing harmful outputs. It's ranked #1 in the OWASP LLM Top 10 because it affects every LLM deployment without proper defenses, requires no special tools or access to execute, and can be delivered through both direct user input and indirect data sources the model processes. Without multi-layer prompt injection protection — input sanitisation, system prompt isolation, output validation, and behavioural monitoring — any LLM application is trivially exploitable.
What is the OWASP LLM Top 10?
The OWASP LLM Top 10 is the authoritative security risk taxonomy for Large Language Model applications, maintained by the same organisation behind the OWASP Web Application Top 10. It covers: LLM01 Prompt Injection, LLM02 Insecure Output Handling, LLM03 Training Data Poisoning, LLM04 Model Denial of Service, LLM05 Supply Chain Vulnerabilities, LLM06 Sensitive Information Disclosure, LLM07 Insecure Plugin Design, LLM08 Excessive Agency, LLM09 Overreliance, and LLM10 Model Theft. Each risk includes attack scenarios, severity assessment, and recommended mitigations. Opsio implements specific, testable controls for every one of these risks.
How is AI security different from traditional cybersecurity?
Traditional cybersecurity protects networks, endpoints, and applications against known attack patterns using firewalls, endpoint detection, and vulnerability scanning. AI security addresses entirely different attack surfaces: natural language inputs that manipulate model behaviour, poisoned training data that embeds backdoors, adversarial examples that fool classifiers, model extraction through systematic API querying, and PII leakage through model outputs. These attacks bypass traditional security tools completely — a WAF cannot detect prompt injection, and antivirus cannot catch a poisoned training dataset. AI security requires specialised expertise, tools, and testing methodologies that most security teams don't yet possess.
How much does AI security assessment and implementation cost?
AI security investment varies by scope. An AI threat assessment covering OWASP LLM Top 10 exposure runs $15,000-$30,000 (1-2 weeks) and delivers a prioritised vulnerability report with remediation roadmap. Security control implementation — guardrails, monitoring, governance, and privacy controls — ranges from $30,000-$65,000 depending on the number of AI systems and integration complexity. A standalone AI red teaming engagement costs $15,000-$30,000. Continuous security monitoring and advisory runs $6,000-$12,000/month. Most organisations start with the threat assessment to understand their exposure before committing to full implementation.
Can you secure self-hosted and open-source LLMs?
Yes — and self-hosted models actually require more security controls than API-based services. With Claude or GPT-4 APIs, the model provider handles infrastructure security and some guardrails. Self-hosted Llama, Mistral, or Qwen deployments on Ollama or vLLM require you to secure the inference infrastructure, model weights, API endpoints, and all guardrails independently. Opsio implements the same defense-in-depth controls for self-hosted models as for API-based ones, plus additional infrastructure hardening, model weight integrity verification, and network security for the serving environment. Self-hosted models offer superior data residency control — no data leaves your network.
What does an AI red teaming engagement involve?
An AI red teaming engagement simulates realistic adversarial attacks against your AI systems over 2-3 weeks. Our red teamers conduct prompt injection campaigns across multiple attack vectors (direct, indirect, recursive), jailbreak attempts using published techniques and novel approaches, system prompt extraction probes, PII and training data extraction attempts, privilege escalation through tool use and function calling chains, denial-of-service attacks against inference infrastructure, and social engineering via AI persona manipulation. Every finding is documented with exploitation evidence, severity rating, and specific remediation steps. We also validate your detection and response capabilities — testing whether your monitoring catches the attacks in progress.
How do you protect against training data poisoning?
Training data poisoning defenses operate at multiple levels. Data provenance tracking ensures every training sample has verified origin and chain of custody. Statistical anomaly detection identifies suspicious patterns in training datasets — unusual label distributions, outlier features, or systematic perturbations. Model behaviour testing after training detects backdoor triggers through activation analysis and clean-label attack detection. For production systems, we implement continuous monitoring that compares model behaviour against baseline expectations, catching performance degradation that may indicate poisoning. Supply chain verification ensures pre-trained model weights and fine-tuning datasets come from trusted, authenticated sources.
Do we need AI security if we only use AI internally?
Yes — internal AI deployments often have weaker security than customer-facing ones, making them attractive targets. Internal LLM tools frequently have access to sensitive company data, intellectual property, financial information, and employee records. A compromised internal AI chatbot connected to your knowledge base could exfiltrate confidential documents. Internal tool-using agents with elevated permissions could be manipulated through prompt injection to perform unauthorized actions. Even without external threat actors, insider risk and accidental data exposure through AI outputs require security controls. Opsio's security framework addresses both external and internal threat models.
How does AI security relate to EU AI Act compliance?
The EU AI Act has specific security requirements for high-risk AI systems under Article 15 (accuracy, robustness, and cybersecurity). High-risk systems must be resilient against attempts to alter their use or performance by exploiting system vulnerabilities — which directly requires protection against adversarial attacks, prompt injection, data poisoning, and model manipulation. Article 9 requires risk management systems that address cybersecurity risks specifically. Opsio's AI security controls are explicitly mapped to EU AI Act requirements, providing documented compliance evidence for conformity assessments and regulatory inspections.
What monitoring do you implement for ongoing AI security?
Continuous AI security monitoring includes: input pattern analysis detecting prompt injection attempts and anomalous query patterns, output monitoring for PII leakage and policy violations, model behaviour drift detection comparing production outputs against baseline distributions, API access anomaly detection for model extraction attempts, dependency vulnerability scanning for ML libraries and pre-trained model components, and security event correlation with your existing SIEM platform. All monitoring feeds into configurable alerting with severity-based escalation to your security operations team. Monthly security reports track threat trends, blocked attacks, and recommendations for control improvements.
Still have questions? Our team is ready to help.
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Traditional security doesn't cover AI threats. Get a free AI threat assessment covering OWASP LLM Top 10 risks and adversarial vulnerabilities.
AI Security & Compliance — Defend the New Attack Surface
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