AI / Enterprise AI & Governance
Embrace AI With Confidence
Strengthen your security posture against emerging threats. End-to-end AI security work that enables safe, compliant adoption — not a checkbox exercise squeezed in before launch.
Turning AI Security Into Architecture
Security that's built in, not bolted on
As AI systems get pulled deeper into how organizations operate, the attack surface grows with them — prompt injection, data poisoning, model drift, and third-party dependencies all introduce risk that traditional security tooling was never built to catch. We treat security as an architectural layer from the first design decision, not a review that happens the week before launch.
Know Your Attack Surface
Eighteen ways AI systems actually get exploited
Grouped by where the risk originates — expand a category to see the specific vulnerabilities inside it.
- Prompt Injection — malicious instructions embedded in input to hijack a model's reasoning or application logic.
- Jailbreaking — deliberate attempts to sidestep guardrails and force outputs the safeguards were built to prevent.
- IP Infringement — a model pulling from external sources risks ingesting protected content it shouldn't have access to.
- IP Theft — attackers prompting a model to regenerate proprietary training data or replicate a competitive edge.
- Model Drift — performance degrades as operational data diverges from training data, opening the door to manipulation.
- Hallucinations — confidently wrong outputs that can drive bad decisions or trigger disastrous automated actions.
- Data Poisoning — adversarial manipulation of retraining data that quietly corrupts model outputs.
- Training Data Exposure — missing privacy masking lets a model ingest and reproduce private information.
- Insecure Data — a larger data surface across pipelines and external databases means more places for vulnerabilities to hide.
- Compliance Risks — sensitive data leakage and broken lineage from improper handling and thin guardrails.
- Unsafe AI Use — models with agentic capability can trigger the wrong action even when guardrails are technically in place.
- Inappropriate Access Control — weak identity management lets bad actors tweak models or users accidentally break them.
- AI-Accelerated Attacks — AI-augmented attacks, deepfakes, and LLM-generated malicious prompts move faster than traditional security tooling.
- Insecure Output Handling — unsanitized outputs opening the door to downstream exploits like XSS or SQL injection.
- Model Denial of Service — flooding a model with excessive requests to spike compute costs and take it offline.
- Supply-Chain Risks — foundation models, agentic frameworks, and third-party tools all add to the overall vulnerability profile.
- Third-Party Dependencies — external dependencies can contaminate outputs and actions in ways your own team never touched.
- Infrastructure Vulnerabilities — misconfigured APIs and middleware are especially dangerous where AI meets legacy systems.
Our Approach
Five stages from context to continuous monitoring
Security work that runs alongside development, not a gate that shows up at the end.
Define Context
Your AI and business goals, critical workflows, and risk tolerance get established so measures support innovation instead of blocking it.
Risk Assessment
AI systems, supporting infrastructure, middleware, and third-party dependencies get evaluated for vulnerabilities and real threats.
AI Strategy & Governance
A security plan gets built into your broader governance framework — access control, endpoint permissions, data transformation, all embedded.
Secure Implementation
A DevSecOps approach integrates high-impact controls across development, deployment, and operational pipelines from day one.
Continuous Monitoring
Ongoing observability tracks model drift and adversarial activity, with proactive adjustments that don't disrupt operations.
What We Deliver
Twelve AI security services
Every layer of the attack surface, covered by one team from assessment through ongoing monitoring.
AI Risk Assessment
Clear risk profiles and remediation strategies, mapped by impact priority.
Agentic AI Security
Mitigating agent manipulation and ensuring orchestration resilience in multi-agent systems.
Model Security & Hardening
Rigorous testing and guardrails that improve resilience against attacks and unpredictable behavior.
Secure Model Fine-Tuning
Proprietary data protection and model integrity through compliant, auditable pipelines.
AI Development Security Consulting
DevSecOps enhancements for scalable, maintainable, and governed AI deployments.
End-to-End Data Security
Data lineage traceability that protects against data poisoning and privacy leaks.
AI Infrastructure & Access Control
Zero-trust principles and robust access control across the whole AI attack surface.
Third-Party AI Risk Management
Secure management of vendor and supply-chain AI dependencies.
Secure AI Deployment & Integration
Safe transformation of AI pilots into production with a hardened security posture.
AI Governance & Compliance Consulting
A governance-lens evaluation that creates tailored remediation strategies, not a generic checklist.
Adversarial Attack Simulation
Red teaming that identifies real attack vectors and scopes the actual attack surface.
Threat & Drift Monitoring
Comprehensive AI observability that catches threats early and keeps operations continuous.
Why Enterprises Choose 11Seas
Six reasons security holds up past the audit
What actually separates AI security that gets certified from AI security that just gets demoed once.
Security-Focused Operations
ISO 27001-aligned practice spanning digital, information, and operational security for AI systems specifically.
Mature AI Expertise
Experience navigating security risk across every stage of AI adoption, deployment, and ongoing operation.
Governance-First Approach
Exhaustive risk profiles and governance roadmaps built before implementation starts, not after.
Regulated Industry Experience
A real understanding of current and evolving AI security risk specific to heavily regulated sectors.
Diverse Cloud Capabilities
Cloud-native governance, security, and data architecture expertise across every major cloud environment.
Performance Optimization
Security controls that scale efficiently and adapt as your AI deployment's parameters keep changing.
AI Security, Risk & Compliance
Unlock AI's potential without raising your risk profile
Security designed to evolve as fast as the threats do — and as fast as your business needs to.
Tools & Standards
Technologies, frameworks & certifications
Security work grounded in the platforms you already run on and the standards your compliance team actually checks against.
Cloud Platforms
Frameworks & Architectures
Standards & Certifications
Where We Work
Built for regulated industries
Frequently
Asked
Questions
Work aimed at making current and upcoming AI development and operations secure against a wide range of external and internal threats — not a single audit, but an ongoing practice.
No — they're distinct domains. AI-augmented cybersecurity uses AI capabilities to strengthen existing security tools; AI security protects the AI systems themselves.
A governance-first approach across development, middleware, and integration, treating security as an architectural layer rather than an add-on — and often hardening your existing digital infrastructure before AI even gets deployed.
Yes — AI security sits under AI governance, with significant overlap with compliance and a modest overlap with ethics.
Yes — private information in fine-tuning or training data, private data surfaced in prompts, and data pulled from internal or external sources an AI system accesses are all in scope.
Unlock AI's potential without raising your risk profile
Evolve and adapt to changing market needs with AI capabilities and efficiencies — and the security posture to back them up.
Tell us about your project!
- Email: hello@11seas.com
- WhatsApp: Message us
- Call: +1 (000) 000-0000
Get clarity on your AI risk profile and compliance needs — in one short call.
