Enterprise AI Security • Sovereign Infrastructure

Sovereign Intelligence

Architecting Deep AI for the Post-Trust Enterprise

AI-generated phishing is up 1,265% since 2023. Deepfake fraud drained $25 million from a single enterprise. The "AI Wrapper" paradigm has failed. Sovereign Intelligence is the only path forward.

Veriprajna deploys private, hardened LLMs within your VPC—zero data egress, full sovereignty, GPT-4-level performance. This whitepaper maps the threat landscape and architects the defense.

Read the Whitepaper
0%
Surge in AI-Generated Phishing Since 2023
$0B
BEC Losses Reported by FBI IC3 in 2024
0%
Click-Through Rate on AI Phishing Emails
ZERO
Data Egress with Veriprajna’s Deep AI

The 2024-2025 Threat Landscape

Generative AI has given attackers nation-state capability at commodity cost. Traditional defenses built on pattern-matching are obsolete.

AI Phishing

82.6% of phishing emails now contain AI-generated content. LLMs eliminate every linguistic "tell" that traditional training relied upon.

Traditional CTR12%
AI-Augmented CTR54%
16 hours → 5 minutes per campaign
95% reduction in attack production cost

Deepfakes

179 incidents in Q1 2025 alone—surpassing all of 2024. Voice cloning now needs only 3-5 minutes of audio.

2022
22
2023
42
2024
150
Q1 '25
179
$25M lost in a single deepfake attack
CFO voice clone bypassed human checkpoints

BEC Fraud

$2.77 billion in 2024 losses. Attackers now use multi-channel "Identity Orchestration"—email, SMS, Teams, and deepfaked calls simultaneously.

Investment Fraud$6.57B
Business Email Compromise$2.77B
Personal Data Breach$1.45B
54% of ransomware begins with phishing
Avg. breach cost: $4.88M (up to $10.22M NA)

The Phishing Evolution

AI didn't just increase the volume of phishing—it fundamentally changed its nature. Every signature-based defense is now obsolete.

inbox — phishing sample

The Failure of the "Wrapper" Paradigm

Enterprise AI built on public API wrappers introduces three catastrophic vulnerabilities that no SLA can mitigate.

01 — Data Egress

Your Data Leaves Your Perimeter

Every prompt, document, and context snippet crosses the public internet to third-party inference servers. Even "Zero Data Retention" tiers retain data for up to 30 days for abuse monitoring.

Prompt → Public Internet → Provider Servers
No technical verification of data handling
02 — Sovereignty

The US CLOUD Act Reach

US-based providers are subject to the CLOUD Act, which compels data disclosure regardless of where servers are located. This creates a direct conflict with GDPR and local data residency laws.

US Law Enforcement → Provider → Your EU Data
Sovereignty requires owning both data & weights
03 — Context

Shadow AI & Hallucination

Wrappers are stateless and hallucinate on proprietary data. When official tools fail, employees use personal accounts on public models—a 485% increase in pasted source code to AI apps.

Employee → Personal ChatGPT → Source Code Leak
72% via accounts beyond corporate visibility

"The prevailing market trend of AI Wrappers—thin interfaces atop public LLM APIs—has proven insufficient for the rigorous security, compliance, and sovereignty requirements of the enterprise. One cannot outsource intelligence and retain control."

— Veriprajna Technical Whitepaper, 2025

Veriprajna Architecture

The Deep AI Stack

Four hardened layers delivering GPT-4-level performance with zero data egress. Every component resides within your VPC.

01

Infrastructure

GPU Orchestration

Full inference stack via K8s on dedicated NVIDIA H100/A100/L40S within your cloud perimeter (AWS, Azure, GCP) or on-premises.

vLLM TGI K8s Strict Egress
02

Model

Open-Weights Hegemony

Best-in-class open-weights models (Llama 3 70B, Mistral, CodeLlama). Own the weights. Immune to provider pricing or "lobotomization."

Llama 3 Mistral LoRA CPT
03

Knowledge

Private RAG 2.0

RBAC-aware retrieval integrated with Active Directory/Okta. If a user can't access a document, the AI can't retrieve it. Prevents contextual privilege escalation.

Milvus Qdrant RBAC Okta/AD
04

Guardrails

Runtime Governance

Real-time I/O analysis via NeMo Guardrails & Cisco AI Defense. Blocks prompt injection, auto-redacts PII/PHI, enforces topic adherence.

NeMo PII Redact Injection Block

The Fine-Tuning Advantage

Deep AI adapts model weights to your organization. Wrappers only adapt prompts.

Prompt Engineering (Wrapper)

Output Consistency85-90%
Domain AccuracyModerate
Token CostHigh
LatencyVariable
Task MasteryGeneralist

Fine-Tuning (Deep AI)

Output Consistency98-99.5%
Domain Accuracy+15% Improvement
Token Cost50-90% Lower
Latency30-60% Lower
Task MasterySpecialist

Defending Against Adversarial ML

As organizations deploy AI for defense, attackers develop techniques to exploit the AI itself. Deep AI must be hardened against both external and model-layer threats.

Evasion Attacks

Input Manipulation

Invisible characters in emails, subtly modified URLs, embedded instructions like "Ignore all previous instructions"—designed to fool AI classifiers into marking malicious input as benign.

Veriprajna Defense

Input Sanitization + Feature Squeezing. All inputs are preprocessed through safety classifiers before reaching the primary model. Suspicious structures are flagged and quarantined.

Data Poisoning

Training/RAG Manipulation

Attackers inject malicious data into training sets or RAG pipelines to create model backdoors. Public API models are inherently vulnerable since their global training corpus is an open attack surface.

Veriprajna Defense

Air-gapped Model Hygiene. Private Enterprise LLMs are trained and grounded exclusively on clean, vetted, internally governed data. The only way to guarantee unsubverted intelligence.

Regulatory Alignment & Trust Architecture

AI governance is now a legal mandate. Non-compliance means fines up to €35M or 7% of global turnover under the EU AI Act.

EU AI Act Compliance

  • Documentation & Traceability

    Immutable logs of every prompt and response ensure a full audit trail for regulatory review.

  • Human-in-the-Loop Triggers

    Agentic workflows auto-escalate high-value decisions (e.g., transfers over $5,000) to human supervisors.

  • Explainability

    Fine-tuned models with transparent architectures are more interpretable than proprietary "black box" APIs.

NIST AI RMF: Four Pillars

Govern
Risk-aware culture
Map
Contextualize harms
Measure
Assess risk
Manage
Respond to risk
The Final Defense

Cryptographic Provenance

When detection fails, provenance prevails. Veriprajna integrates C2PA (Coalition for Content Provenance and Authenticity) to cryptographically sign digital assets at the point of origin.

Executives can "true-sign" video or voice authorizations, linking verified legal identity to the digital record. Attackers cannot forge the cryptographic signature—eliminating voice-clone BEC.

Executive
Biometric ID
C2PA Sign
Crypto Manifest
Asset
Video/Audio/Doc
Verified
Tamper-Evident
Deepfake modification → Crypto manifest breaks → Warning displayed
Economic Justification

From OPEX to Asset Development

Public LLMs are unpredictable OPEX. Private deployment converts rented intelligence into a proprietary asset with near-zero marginal cost.

Private LLM Savings Calculator

Estimate your annual savings by switching from public API to self-hosted inference

100M
$10.00
$3,000
API Cost / Year
$120K
Annual Savings
$84K

Security & Performance KPIs

Metrics we track for every Deep AI deployment

Threat Detection
Mean Time to Detect (MTTD)
Avg. time to identify an AI-mediated attack
Response
Mean Time to Remediate (MTTR)
Time to contain and resolve the incident
AI Quality
Hallucination Rate
Frequency of factually incorrect outputs
Operational
Semantic Drift
Model performance degradation over time
Identity
Auth Success Rate
Correctly verified executive identity signatures
Governance
Shadow AI Incidents
Unauthorized AI tools detected in environment

Is Your AI an Asset—or a Liability?

In the post-trust enterprise, the ultimate competitive advantage is not just intelligence, but the ability to verify it.

Veriprajna deploys Sovereign Intelligence—private LLMs, zero egress, full regulatory alignment. Let us architect your defense.

Security Assessment

  • Shadow AI audit of your current tool landscape
  • Data egress risk analysis for existing AI integrations
  • EU AI Act / NIST AI RMF gap assessment
  • Custom ROI model for private LLM deployment

Pilot Deployment

  • Private LLM stack provisioned in your VPC
  • RAG 2.0 integration with your knowledge base
  • Guardrails configuration and adversarial testing
  • Comprehensive post-pilot performance report
Connect via WhatsApp
Read the Full Technical Whitepaper

Complete report: Threat landscape data, Deep AI architecture, adversarial ML defenses, NIST/EU AI Act alignment, economic models, and security KPIs.