Secure Frameworks
for AI Are No Longer Optional
As AI adoption explodes across the enterprise, so do the unique risks that traditional cybersecurity cannot address. secureframeworks.ai is the authoritative domain at the intersection of AI and security.
The Attack Surface
AI Introduces Threats Traditional Security Cannot Address
These are not hypothetical vulnerabilities. Every one of these attack vectors is being actively exploited against production AI systems today.
Prompt Injection
Malicious inputs hijack model behavior — the #1 OWASP LLM risk in 2025. Both direct and indirect injection are actively exploited in production systems.
Supply Chain Vulnerabilities
Poisoned datasets, pre-trained models, and third-party plugins introduce hidden attack surfaces throughout the AI lifecycle.
Data & Model Poisoning
Adversarial manipulation during training degrades model integrity at the source — often undetectable until deployment.
Model Extraction
Attackers reconstruct proprietary models via repeated API queries — avg. loss exceeds $2.3M when including R&D costs.
Sensitive Information Disclosure
Training data, system prompts, PII, and embeddings leak through model outputs — often unintentionally, at scale.
Excessive Agency
Autonomous AI agents take unintended, high-impact actions without adequate human oversight or sandboxing.
System Prompt Leakage
Carefully crafted inputs can extract confidential system-level instructions, exposing business logic and security controls.
Vector & Embedding Weaknesses
RAG architectures and embedding stores introduce novel attack surfaces including indirect injection via retrieved documents.
Improper Output Handling
Downstream components blindly trusting LLM output enables XSS, SSRF, privilege escalation, and remote code execution.
OWASP LLM Top 10 — 2025
The Definitive Developer Checklist for LLM Security
The OWASP Top 10 for Large Language Model Applications (2025 edition) is the industry-standard reference for identifying, prioritizing, and mitigating the most critical risks in AI-powered applications.
Mandatory Cybersecurity Requirements for High-Risk AI
The EU AI Act Article 15 mandates that high-risk AI systems must be designed to achieve appropriate levels of accuracy, robustness, and cybersecurity throughout their entire lifecycle. Providers of general-purpose AI models with systemic risk must also conduct adversarial testing and maintain adequate cybersecurity posture.
Read the EU AI Act →Explicit Resilience Requirements:
Non-Compliance Penalties
Up to €35 million or 7% of global annual turnover — whichever is higher.
Authoritative Standards
The Leading AI Security Frameworks
Organizations that embed these frameworks early reduce breach recovery time by 38%, accelerate regulatory compliance, and protect intellectual property at every layer of the AI stack.
NIST AI RMF 1.0
Govern → Map → Measure → Manage. The U.S. government standard for trustworthy AI — now with a Generative AI Profile (July 2024) and Critical Infrastructure Profile (April 2026).
OWASP Top 10 for LLMs (2025)
The definitive developer checklist: 10 critical risks from Prompt Injection to Unbounded Consumption, with detailed mitigations for each.
EU AI Act — Article 15
High-risk AI must achieve accuracy, robustness, and cybersecurity throughout its lifecycle. Explicit resilience requirements against poisoning, evasion, and confidentiality attacks.
MITRE ATLAS
Adversarial Threat Landscape for AI Systems — the definitive catalog of real-world AI attack tactics and techniques mapped from actual incidents.
Google SAIF
Google's Secure AI Framework covers 15+ practitioner-oriented risks and controls for building resilient AI systems at scale.
Databricks DASF 2.0
62 identified AI security risks mapped to actionable controls — built for data and ML engineering teams deploying production models.
ENISA FAICP
European Union Agency for Cybersecurity framework for AI cybersecurity practices, aligned with the EU AI Act requirements.
ISO/IEC 42001
The international AI management system standard — defines requirements for responsible AI development, deployment, and governance at the organizational level.
Business & Regulatory Imperative
The Window to Act Proactively Is Closing
Organizations that embed secure AI frameworks early reduce breach costs, accelerate compliance, protect intellectual property, and maintain customer trust in an era of increasing AI scrutiny.
EU AI Act
Mandatory compliance for high-risk AI systems across the EU. Non-compliance penalties up to €35M or 7% of global turnover.
ISO/IEC 42001
International AI management system standard. Accelerates internal governance and supplier due diligence.
NIST AI RMF
Voluntary U.S. framework becoming contractual baseline in federal procurement and supply chain requirements.
U.S. Executive Orders
Ongoing federal AI safety mandates and forthcoming sector-specific rules across critical infrastructure.
Market Opportunity
13.3% CAGR from $6.7B in 2023
IBM 2025 Cost of a Data Breach
Domain Available
secureframeworks.ai
A premium, exact-match domain at the intersection of the two most critical technology themes of the decade.
Contact sales@desertrich.comThe Bottom Line
Secure frameworks for AI are the foundation
for trustworthy, resilient deployment.
This domain positions you at the center of a $15.9B market growing at 13.3% CAGR. Reach out to discuss acquisition.
Inquire Now — sales@desertrich.com