Overview

Product details compiled from public sources, each with a citation.

Vendor: Securiti1
Description: Data and AI security command center that discovers and classifies sensitive data across the enterprise and runs context-aware LLM firewalls over AI prompts, retrieval, and responses.1
Deployment: SaaS1
Status: Active1

Matrix Coverage

Where this product defends, by asset class and NIST CSF function. The Coverage column shows whether each asset is Primary, Secondary, or Adjacent to what the product does. The table omits empty rows and columns.

Asset class	Identify	Protect	Detect	Coverage	Source
Training Data	Identify: Covered	Protect: Covered	Detect: Not covered	Primary	2
Runtime AI Data	Identify: Not covered	Protect: Covered	Detect: Covered	Secondary	1

Framework Relevance

These frameworks include controls relevant to the asset classes Securiti defends. This is an editorial inference from the AI Defense Matrix asset-level crossmap, not a statement that Securiti implements these controls or is certified against them.

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Framework	Asset class	Relevant controls
NIST IR 8596	Training Data	Training data
NIST IR 8596	Runtime AI Data	Prompts (runtime); inference data
CSA AI Controls Matrix	Training Data	Data Security and Privacy Lifecycle Management; Model Security
CSA AI Controls Matrix	Runtime AI Data	Data Security and Privacy Lifecycle Management; Application and Interface Security
ISO 42001	Training Data	A.7 Data for AI systems
ISO 42001	Runtime AI Data	A.7 Data for AI systems; A.8 Information for interested parties
Google SAIF	Training Data	Secure training data; data-security foundations; dataset provenance and integrity
Google SAIF	Runtime AI Data	Expand AI red-teaming; runtime input and output safety; prompt defense
SANS Critical AI Security Guidelines	Training Data	Conventional Security Controls (defend training data; avoid data commingling); Data/Model Engineering Controls (data-quality controls; poison-robust training); Data Minimization and Obfuscation (differential privacy; synthetic data; federated learning)
SANS Critical AI Security Guidelines	Runtime AI Data	Model I/O Handling (sanitize, validate, and filter inputs and outputs; segregate user and system prompts; multilayered prompt-injection defense); Conventional Security Controls (protect augmentation and RAG data with vector-store access controls and validation); Data Minimization and Obfuscation (limit sensitive prompt content; context-window management); Limit Model Behavior (AI guardrails)
MITRE ATLAS	Training Data	AML.T0020 Poison Training Data; AML.T0019 Publish Poisoned Datasets; AML.T0024.000 Infer Training Data Membership
MITRE ATLAS	Runtime AI Data	AML.T0051 LLM Prompt Injection; AML.T0054 LLM Jailbreak; AML.T0056 Extract LLM System Prompt
OWASP AI Exchange	Training Data	Development-time threats: data poisoning, backdoor injection, dataset integrity violations
OWASP AI Exchange	Runtime AI Data	Input threats: prompt injection, adversarial inputs, evasion; runtime threats: RAG poisoning, memory tampering
OWASP LLM Top 10	Training Data	LLM04 Data and Model Poisoning; LLM03 Supply Chain (dataset provenance)
OWASP LLM Top 10	Runtime AI Data	LLM01 Prompt Injection; LLM02 Sensitive Information Disclosure; LLM08 Vector and Embedding Weaknesses; LLM05 Improper Output Handling
OWASP Agentic Security Top 10	Training Data	ASI04 Agentic Supply Chain Vulnerabilities (dataset provenance and integrity)
OWASP Agentic Security Top 10	Runtime AI Data	ASI06 Memory & Context Poisoning; ASI01 Agent Goal Hijack (via prompt injection in runtime inputs)

Provenance

Last sourced 2026-06-09.

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Sources

Securiti Data and AI security
Vendor source accessed 2026-06-09
Securiti Data Security Posture Management
Vendor source accessed 2026-06-09

Changelog

2026-06-09
Added to the catalog from the Securiti documentation.

Found an error? Corrections are welcome. Suggest an edit.

Product Strategy and Positioning

You can use the following frameworks to understand the product’s strategy and its competitive positioning. Performing this analysis is outside the scope of the AI Defense Matrix Catalog, but the following guidance can help you with such an assessment.

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Product Strategy

Lenny Zeltser’s Guide to Creating Cybersecurity Products can help you understand key aspects of the product strategy. You can use your AI tool to gather the data and apply this framework.

Market segment: Who the product is built for: industry, size, and the persona who evaluates it.
Go-to-market motion: How it reaches buyers: top-down sales, bottom-up adoption, or open source.
Pricing model: How value is captured: per-seat, consumption, or outcome-based.
Delivery and operations: How it is deployed, configured, and maintained, including infrastructure-as-code and API coverage.
Customer trust: Certifications, transparency, and supply-chain security a buyer expects from the vendor.
Ecosystem position: A point solution, a platform others build on, or a component of a larger platform.

Strategy Defensibility

Ben Vierck’s rubric can help you assess the defensibility of the SaaS product’s strategy against competitive and other market forces. You can use it with your AI tool for a methodical analysis.

Value delivery: How much of the value is hard to replicate versus standard software a competitor could rebuild.
Switching cost: How costly it is to leave once deployed: integrations, data, workflow, and platform ties.
Compliance moat: Whether certifications or regulatory alignment are a durable advantage or table stakes for this buyer.
Problem complexity: How hard, adversarial, and fast-moving the underlying problem is to solve well.
Buyer profile: Who holds the budget, and how durable that demand is across the market.
Layer: Where the product operates: application, model, infrastructure, platform, or identity control plane.
Proprietary data, content, or IP: Whether it accumulates data, content, or IP that others would find difficult to replicate.