Bonfy ACS
Overview
Product details compiled from public sources, each with a citation.
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 |
|---|---|---|---|---|---|
| AI-Workload Platforms | Protect: Not covered | Secondary | 5 | ||
| AI Orchestration Tools | Identify: Not covered | Secondary | 4 | ||
| Runtime AI Data | Identify: Not covered | Primary | 3 |
Framework Relevance
These frameworks include controls relevant to the asset classes Bonfy ACS defends. This is an editorial inference from the AI Defense Matrix asset-level crossmap, not a statement that Bonfy.AI implements these controls or is certified against them.
Expand Collapse
| Framework | Asset class | Relevant controls |
|---|---|---|
| NIST IR 8596 | AI-Workload Platforms | Containers, microservices, and libraries (AI-specific subset); inference endpoints (platform side) |
| AI Orchestration Tools | Agents as deployed artifacts (orchestration view; see AI Agent Identities row for the principal view); system prompts and templates | |
| Runtime AI Data | Prompts (runtime); inference data | |
| CSA AI Controls Matrix | AI-Workload Platforms | Infrastructure Security; Threat & Vulnerability Management |
| AI Orchestration Tools | Application and Interface Security; Supply Chain Management | |
| Runtime AI Data | Data Security and Privacy Lifecycle Management; Application and Interface Security | |
| ISO 42001 | AI-Workload Platforms | A.6 AI system life cycle; A.4 Resources for AI systems |
| AI Orchestration Tools | A.6 AI system life cycle; A.5 Assessing impacts of AI systems | |
| Runtime AI Data | A.7 Data for AI systems; A.8 Information for interested parties | |
| Google SAIF | AI-Workload Platforms | Expand strong security foundations; secure and harden the AI deployment environment |
| AI Orchestration Tools | Secure the AI supply chain; application and pipeline security; agent orchestration controls | |
| Runtime AI Data | Expand AI red-teaming; runtime input and output safety; prompt defense | |
| SANS Critical AI Security Guidelines | AI-Workload Platforms | Conventional Security Controls (host AI within the existing ISMS; authentication and access controls; encryption at rest); AI Supply Chain Management (local vs. SaaS hosting trade-offs; internal model garden) |
| AI Orchestration Tools | Secure Agentic Systems and AI Autonomy Controls (defined function scope; execution isolation; API and function-call gating); Limit Model Behavior (focused functionality; access controls outside the model) | |
| 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 | AI-Workload Platforms | AML.T0010 AI Supply Chain Compromise; AML.T0012 Valid Accounts (platform credential abuse); container and inference-server exploits |
| AI Orchestration Tools | AML.T0051 LLM Prompt Injection; AML.T0054 LLM Jailbreak; AML.T0016 Obtain Capabilities (malicious plugins) | |
| Runtime AI Data | AML.T0051 LLM Prompt Injection; AML.T0054 LLM Jailbreak; AML.T0056 Extract LLM System Prompt | |
| OWASP AI Exchange | AI-Workload Platforms | Development-time threats: supply chain attacks, model-platform CVEs, container escape |
| AI Orchestration Tools | Development-time threats: agent framework supply chain; runtime threats: plugin abuse, prompt injection via tools | |
| Runtime AI Data | Input threats: prompt injection, adversarial inputs, evasion; runtime threats: RAG poisoning, memory tampering | |
| OWASP LLM Top 10 | AI-Workload Platforms | LLM03 Supply Chain (compromised AI platform components); LLM04 Data and Model Poisoning (via platform) |
| AI Orchestration Tools | LLM01 Prompt Injection; LLM05 Improper Output Handling; LLM07 System Prompt Leakage; LLM10 Unbounded Consumption | |
| Runtime AI Data | LLM01 Prompt Injection; LLM02 Sensitive Information Disclosure; LLM08 Vector and Embedding Weaknesses; LLM05 Improper Output Handling | |
| OWASP Agentic Security Top 10 | AI-Workload Platforms | ASI04 Agentic Supply Chain Vulnerabilities (model and tool-platform components); ASI08 Cascading Failures (platform fault propagation) |
| AI Orchestration Tools | ASI01 Agent Goal Hijack; ASI02 Tool Misuse and Exploitation; ASI05 Unexpected Code Execution (RCE); ASI07 Insecure Inter-Agent Communication; ASI08 Cascading Failures; ASI10 Rogue Agents | |
| Runtime AI Data | ASI06 Memory & Context Poisoning; ASI01 Agent Goal Hijack (via prompt injection in runtime inputs) |
Provenance
Last sourced 2026-06-10.
Expand Collapse
Sources
- Bonfy ACS 2.0 press release
- Bonfy.AI launch press release
- Bonfy ACS product page
- “Bonfy ACS solves the needs for data in motion and data at rest risks, and provides the options to use the solution either inline, offline, and/or in retrospective modes.”
- Bonfy agentic data security use case
- “Secure the full agent lifecycle: inputs, outputs, and data-in-use inspection via MCP.”
- Bonfy shadow AI security use case
- “Detect Shadow AI usage patterns across users, teams, and locations”
Changelog
-
Added to the catalog from the Bonfy.AI documentation.
Found an error? Corrections are welcome. Suggest an edit.