Inside MDASH: Designing a Microsoft‑Scale Multi‑Model Agentic Cyber Defense Benchmark

Originally published on CoreProse KB-incidents

Agentic LLMs already sit in the critical path of security operations: enriching SIEM alerts, driving SOAR playbooks, reviewing code, and proposing firewall changes. Yet many teams still measure them like chatbots—on single‑prompt accuracy—rather than as end‑to‑end, multi‑model, safety‑critical systems.

A MDASH‑style benchmark (Multi‑model, Data‑driven, Agentic Security Harness) changes this. It treats SOC and SDLC as a single defensive fabric and evaluates the full architecture—from data layer to tool calls—under realistic attack, noise, and governance constraints.[2][3]

Goal of this article

This guide outlines how to design such a benchmark:

• Why MDASH‑style benchmarks matter now
• The reference multi‑agent architecture
• Threat model and scenario design
• Metrics and methodology
• Implementation blueprint
• Governance and rollout considerations

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1. Why a MDASH‑Style Multi‑Model Agentic Cyber Defense Benchmark Matters

Classic SOC capacity scaled with analyst headcount and expertise: more telemetry meant more humans or more missed alerts.[2] LLM‑based SOCs break this curve, shifting the bottleneck to data architecture and orchestration quality.[3]

Evidence from LLM‑augmented SOCs shows a single model can:

• Correlate large log volumes
• Fuse telemetry with threat intel
• Produce high‑fidelity incident summaries in under a minute[3]

Previously, this consumed hours of senior analyst time. Measurement must therefore move from “model quality in isolation” to system‑level impact on time‑to‑detect and time‑to‑respond.

Providers are also shipping cyber‑specific stacks like GPT‑5.5 with Trusted Access for Cyber (TAC) and GPT‑5.5‑Cyber, tuned for malware triage, reverse engineering, and critical‑infrastructure defense.[4][6] We now need benchmarks comparing agentic system designs, not just prompt engineering or single‑turn QA.

New attack surface

Agentic AI is itself an attack surface. Agents:

• Call tools and run code
• Access SIEM, EDR, ticketing, CI/CD
• Talk to internal services via protocols like MCP[7]

Every new capability introduces failure modes: prompt injection, data exfiltration, tool abuse, unsafe code execution.[1][8]

Industry guidance stresses that agent security depends as much on planning, memory, and tool‑use controls as on base‑model alignment.[7][8] A meaningful benchmark must cover:

• Detection and triage quality
• Orchestration behavior under load
• Safety and policy adherence under adversarial pressure

Concrete example

A 5,000‑employee SaaS company piloted an LLM triage assistant on top of its SIEM. It:

• Cut median alert review time by ~60%
• But auto‑closed a few low‑volume, high‑impact lateral‑movement alerts because orchestration over‑trusted a noisy EDR feed[2][3]

A MDASH‑style benchmark with noisy, adversarial telemetry and explicit metrics for missed critical incidents would have exposed this.

Mini‑conclusion

MDASH matters because cyber‑AI is now about architected, multi‑model agent systems that must be evaluated end‑to‑end, including safety controls and data plumbing.[3][4][7]

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2. Conceptual Architecture of a Multi‑Model Agentic Cyber Defense System

MDASH starts from a clear reference architecture: a hierarchy of cooperating agents with explicit roles, tools, and guardrails.[2][5][7]

2.1 Core agent hierarchy

Typical roles:

• Top‑level Security Orchestrator

  • Receives tasks (e.g., triage batch, assess incident, review repo)
  • Delegates to sub‑agents, tracks state, synthesizes outcomes[3][7]

• SOC Triage Agent

  • Connects to SIEM/EDR
  • Enriches alerts, correlates sources, proposes severity and playbooks[2]

• Threat Hunting Agent

  • Tests hypotheses over historical logs, intel, knowledge bases

• Code & SDLC Security Agent

  • Integrates with Git, CI, and SCA tools
  • Builds threat models, finds attack paths, tests patches in sandboxes[5][6]

• Tool Executor / Actuator Agents

  • Wrap high‑risk operations (firewall changes, account lockdowns, patch deployment)
  • Enforce tighter policies and human approval paths[1][4]

Databricks’ Agentic AI extension treats planning, memory, and tool use as separate risk‑bearing components and recommends dedicated controls for each.[7] MDASH architectures should mirror this with:

• A planner service
• A memory store
• A tool router

so each can be independently evaluated and hardened.

Architecture as data‑flow diagram

From a security‑engineering view, MDASH should be documented as a data‑flow diagram:

1. SIEM/EDR logs and traces → preprocessing → feature/embedding stores
2. Retrieval and RAG over knowledge bases and incident history
3. Multi‑model reasoning (e.g., GPT‑5.5 for orchestration, GPT‑5.5‑Cyber for deep analysis)[4][6]
4. Tool invocations via MCP or similar connectors
5. Outputs (tickets, SOAR actions, code changes) routed through governance layers[1][7][8]

Each hop becomes an evaluation point for latency, correctness, and safety.[3][7]

Policy enforcement points

Because agents bridge sensitive internal data and untrusted inputs, Databricks recommends layered controls around:[1][7][8]

• Data access: least privilege, row/column filters
• Input validation: sanitizing prompts, constraining tool arguments
• Output restriction: limiting what can be executed or persisted

Your reference architecture should mark policy enforcement points before tools, data connectors, and external APIs. MDASH will probe these for failures.

Mini‑conclusion

The MDASH architecture is not “one big agent with tools,” but a set of separated planners, workers, and governors, each measurable and hardenable on its own.[2][5][7]

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3. Benchmark Scope, Threat Model and Scenarios for MDASH

With the architecture defined, MDASH next specifies what to test: a threat model and scenario set that mirror modern SOC and SDLC realities.[2][3]

3.1 Threat model

Key elements:

• High alert volume and fatigue – thousands of low‑signal alerts per day[2]
• APTs and multi‑stage kill chains – stealthy, long‑lived campaigns
• Complex internal estates – legacy systems, weak segmentation, shadow IT[3]
• Adversarial AI use – automated recon, exploit generation, social engineering[4][6]

MDASH assumes both benign noise and intelligent adversaries shaping telemetry and context.

3.2 SOC‑aligned scenarios

Current SOC AI deployments automate SIEM triage, enrichment, and incident qualification.[2] MDASH builds on this with scenarios such as:

• Credential‑stuffing bursts with a few real compromises hidden inside
• Slow lateral movement using legitimate tools and low‑noise signals
• Suspicious binary on a critical server requiring malware triage and recommendations[2][3][4]

For each, the benchmark injects synthetic or replayed attacks and measures:

• Time‑to‑correct‑classification
• False‑negative and false‑positive rates
• Analyst workload reduction and escalation patterns

Adversarial agent scenarios

LLM and agent security work highlights vulnerabilities to:[1][8]

• Direct and indirect prompt injection
• RAG/knowledge‑base poisoning
• Malicious tool responses
• Jailbreaks and data‑exfil prompts

MDASH should include:

• Hostile instructions hidden in logs or docs
• Poisoned RAG corpora trying to override policies
• Tools that return adversarial outputs (e.g., spoofed privileges)

and measure whether agents still enforce policy and trigger safeguards.[1][7][8]

3.3 SDLC and product security scenarios

Daybreak embeds security into SDLC via secure code review, attack‑path modeling, dependency analysis, and sandboxed patch validation.[5][6] MDASH should mirror this with scenarios for:

• Detecting critical vulnerabilities in realistic repos
• Generating threat models from code and infrastructure definitions
• Proposing patches and validating them in sandboxes[5][6]

Because GPT‑5.5 and GPT‑5.5‑Cyber target different defensive tiers—from enterprise SOC to critical infrastructure and red‑team‑style tasks—scenarios should be tagged by operational tier and expected control strength.[4][6]

Reactive vs autonomous

Modern SOCs move from purely reactive triage to more autonomous defense, where agents:

• Continuously monitor
• Surface anomalies
• Propose pre‑emptive actions[3]

MDASH should distinguish:

• Reactive tasks – classify and enrich static alert batches
• Autonomous tasks – continuous monitoring, anomaly surfacing, pre‑emptive hardening

with separate success metrics and safety expectations.

Mini‑conclusion

MDASH’s value comes from scenarios that span SOC triage, adversarial agent behavior, and SDLC security, grounded in realistic operational tiers and attacker behaviors.[2][3][5][8]

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4. Evaluation Dimensions, Metrics and Methodology

MDASH then defines how to score systems across accuracy, performance, and safety.

4.1 Accuracy and efficiency metrics

For alert triage, core metrics include:[2]

• Precision and recall per severity band
• Time‑to‑triage (p50/p95)
• Escalation rate to humans and downstream re‑open rate

To capture SOC scalability, measure reduction in analyst time per incident against a manual baseline, reflecting that LLM‑driven designs move bottlenecks to data and orchestration layers.[3]

Latency and throughput

Multi‑model pipelines chain embeddings, retrieval, reasoning, and tool calls.[4] MDASH should log:

• End‑to‑end latency: alert ingestion → recommended action
• Per‑stage latency: RAG, LLM reasoning, each tool call
• Throughput under realistic alert volume and concurrency[2][4]

These determine feasibility for near‑real‑time detection and response.

4.2 Safety and robustness metrics

Building on Databricks’ layered controls and Rule of Two guidance, MDASH should track:[1][7][8]

• Prompt‑injection success rate (agent performs disallowed action)
• Policy‑violation rate (attempted access to forbidden data or tools)
• Malformed or unsafe tool invocation frequency
• Misuse of long‑term memory (persistence of malicious instructions)[7][8]

Each adversarial scenario should output:

• An effectiveness score – did the attack evade detection?
• A resilience score – were controls engaged, was it logged, were users alerted?

Planning, memory, and tool connectivity

Agentic AI frameworks emphasize new risks around:[7]

• Long‑term memory correctness and sanitization
• Multi‑step plan safety and checkpointing
• Handling untrusted tool outputs via MCP and similar protocols

MDASH can provide sub‑scores such as:

• Safe memory use
• Correct multi‑step planning
• Safe tool mediation and response validation

4.3 SDLC‑specific metrics

Inspired by Daybreak workflows, SDLC metrics should cover:[5][6]

• Vulnerability detection coverage vs ground truth
• False‑positive rate in scans
• Mean time from detection to sandbox‑validated patch
• Quality and completeness of generated security documentation

Methodology and reproducibility

Every MDASH run should log:[1][2][4][8]

• Model versions and configs (e.g., GPT‑5.5 vs GPT‑5.5‑Cyber, temperature)
• System prompts and templates
• Tool configurations and permissions
• Data slices, scenario IDs, and seeds

LLM security guides stress reproducibility and auditability for regimes like NIS2 and DORA.[8] MDASH results must be replayable and attributable.

Mini‑conclusion

MDASH evaluates far beyond “was the answer correct?” It measures accuracy, latency, safety, and SDLC outcomes under an auditable, repeatable methodology.[1][2][4][7]

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5. Implementation Blueprint: From Data to Multi‑Model Agent Orchestration

MDASH must run on top of existing SOC and SDLC stacks.

5.1 Data and retrieval layer

Instrument the SOC data layer—SIEM, EDR, asset inventories, threat intel—into a structured store accessible via tools.[2][3] Typically:

• Normalize telemetry into a unified schema
• Build indexed stores (columnar for logs, vector for text)
• Expose read‑only, least‑privilege interfaces for agents[1][8]

On top, implement a retrieval layer with vector search and hybrid filtering (KNN + metadata). This layer is also an attack surface: RAG corpora can be poisoned with malicious instructions.[1][8]

Guarding retrieval

Apply Databricks‑style layered controls:[1][7][8]

• Filter and sanitize ingested documents
• Restrict which collections each agent can query
• Post‑process retrieved chunks to strip executable instructions when feasible

5.2 Agent orchestration and role separation

Use an agent framework (custom, LangGraph‑like, or MCP‑based) to encode role separation:[7]

• Planner agent – interprets tasks, produces plans and sub‑tasks
• Worker agents – execute specific tool calls (queries, EDR actions, ticket updates, CI runs)
• Governance agent – enforces policies, performs “second opinion” checks, logs rationales for audit[1][7]

This reflects Databricks’ separation of planning, memory, and tool execution for risk analysis.[7]

Code and SDLC path

To mirror Daybreak, define a dedicated SDLC agent wired to:[5][6]

• VCS (Git) for diffs and history
• SCA/SAST tools for dependency and code analysis
• CI systems for sandbox tests

Run it with strict least privilege and only against non‑production. It should output patches and validation artifacts for human or higher‑tier agent approval.

5.3 Control plane and monitoring

Because agents can trigger real‑world actions, implement a control plane that:

• Classifies actions by risk tier
• Requires human approval or multi‑signal validation (Rule of Two) for high‑risk steps
• Applies policy‑as‑code checks before execution[1][7]

Log all prompts, intermediate reasoning, tool calls, and decisions back into security monitoring pipelines, aligning with guidance that LLM I/O must be filtered, monitored, and governed.[2][8]

Model selection strategy

For MDASH experiments:

• Use general models like GPT‑5.5 for orchestration and broad reasoning
• Use specialized models like GPT‑5.5‑Cyber for deep security analysis, reverse engineering, and red‑team‑style tasks[4][6]

MDASH itself should remain model‑agnostic, centering on tasks, data, and metrics so vendors and configurations can be compared fairly.

Mini‑conclusion

An implementation‑ready MDASH system combines structured data, guarded retrieval, role‑separated agents, and a strong control plane into a coherent, observable cyber‑defense fabric.[1][3][5][7]

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6. Governance, Safety and Production Rollout Considerations

MDASH is only valuable if it informs governance and risk management, not just lab demos.

6.1 From benchmark to risk register

LLM and agent security guides frame these systems as a new, highly exposed attack surface that must be part of the organization’s overall threat model.[7][8] MDASH outputs should:

• Feed into the enterprise risk register
• Inform security architecture and design reviews
• Drive updates to SOAR and incident response playbooks[2][7]

Databricks’ Agentic AI extension lists 35 new technical risks and six mitigation controls focused on memory, planning, and MCP tool use.[7] MDASH should maintain a coverage checklist mapping which risks each scenario exercises.

Measuring hardened vs baseline configs

Prompt‑injection mitigation guidance favors defense‑in‑depth: strict data access, input validation, output restriction.[1] MDASH should compare:

• Baseline configuration (minimal controls)
• Hardened configuration (full layered controls)

and report performance and usability deltas to clarify trade‑offs between safety and speed.[1][8]

6.2 Aligning with provider safeguards and regulation

As providers ship trusted access models and specialized cyber offerings with proportional safeguards, MDASH‑driven decisions should align with those guardrails.[4][6] For example:

• Use GPT‑5.5‑Cyber only for authorized red‑team and high‑risk defensive workflows, in line with internal policies and regulation[4][6]
• Prefer trusted access channels (e.g., TAC) for sensitive data flows, and benchmark configurations with and without those safeguards enabled[4]

Mini‑conclusion

A well‑governed MDASH program turns agentic cyber defense from an experiment into a controlled, auditable capability—integrated with risk registers, aligned with provider safeguards, and evolvable over time.[2][4][7][8]

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