TL;DR

 
For those who’ve done this before and just need the answers, we provide the following:
  • We provide a one-off AI/ML security and enablement engagement—covering discovery, design, guardrails, and validation—for scopes of any size or complexity.
  • We turn your disparate AI experiments and AppSec/ML ops efforts into a singular AI/ML security capability that costs less than the sum of its current parts.
  • We bring ILT, hard-won expertise, ongoing support, and—if you desire—LLM-based technology (Aedify + Manicode.ai promptbook IP) to seed developer, architect, and sign-off agents.
  • We scale your AI/ML security practice, aligning stakeholders and federating it to Champions or Developers.
How it works:
  • Firm fixed price AI/ML security and enablement engagement; small, medium, and large timelines are typically four, six, and ten weeks.
  • Firm fixed price or time-and-materials for capability building; usually takes three to six months.
  • Retainer agreement for ongoing mentoring, coaching, and skill building; most effective when done for twelve months.
For those who are learning more about these areas, please consider the descriptions below.
Ship secure AI, not AI security theater. We help leadership teams put AI to work where it matters: strengthening engineering throughput, governing risk at scale, and reducing AppSec friction. The problem isn’t just talent scarcity or tool sprawl—it’s that legacy approaches don’t adapt fast enough to modern delivery.
 

The reality you’re living in:

  • Staffing is tight; everyone wants the same multi-skilled unicorns.
  • Enterprise-wide secure coding standards create friction and stall adoption.
  • AppSec tools grow the bug pile; it takes exponentially more people to burn it down.
  • Every greenfield or major refactor seems to add another stack to support.
  • For every 10 new apps, you typically need 30–50 dev/QA plus at least one security champion, one security architect, and a change to your sign-off process.
  • “Security via testing tools alone” hasn’t accelerated maturity for a decade—and it won’t next year either.
We bring a pragmatic, model-agnostic approach: use AI to build security in, govern what’s already running, and harden the AI you deploy—without asking engineering to slow down or change tools.

Strategy and Culture Collaboration

Ship secure AI, not AI security theater. We help leadership teams put AI to work where it matters: strengthening engineering throughput, governing risk at scale, and reducing AppSec friction. The problem isn’t just talent scarcity or tool sprawl—it’s that legacy approaches don’t adapt fast enough to modern delivery.
  • The reality you’re living
  • Staffing is tight; everyone wants the same multi-skilled unicorns.
  • Enterprise-wide secure coding standards create friction and stall adoption.
  • AppSec tools grow the bug pile; it takes exponentially more people to burn it down.
  • Every greenfield or major refactor seems to add another stack to support.
  • For every 10 new apps, you typically need 30–50 dev/QA plus at least one security champion, one security architect, and a change to your sign-off process.
  • “Security via testing tools alone” hasn’t accelerated maturity for a decade—and it won’t next year either.
We bring a pragmatic, model-agnostic approach: use AI to build security in, govern what’s already running, and harden the AI you deploy—without asking engineering to slow down or change tools.

Shadow AI/ IT Discovery and Control

Gain visibility into unapproved AI usage and the data it touches—then apply policy-driven controls. Using cutting-edge discovery and governance (e.g., jiffy.ai), we help you see AI adoption patterns and act through a unified control plane.
  • How It Works:
    • Discover shadow AI usage across teams, tools, and SaaS.
      • Inventory models, services, plugins, browser extensions, and website use; and
      • Surface data flows in and out of AI tools.
    • Classify usage and data sensitivity against your policies.
      • Map users, teams, and use cases to acceptable use, prohibited patterns, and gray areas requiring review;
      • Surface connections between AI agents or persistence stores; and
      • Inventory RAG, Vector, and other data sources. 
    • Apply controls and guardrails where appropriate.
      • Enforce policy via identity, network, and application-level guardrails to deny, allow, or conditionally allow usage; and
      • Provide a policy layer that enforces only approved product and LLM use, in concert with approved data sources. 
    • Establish ongoing governance.
      • Create review workflows, decision logs, and exception handling to keep pace with change.
      • Apply compulsory DLP and audit.  
  • What You Get
    • AI usage inventory with risk classifications and data exposure views.
    • A practical control plane to block, allow, or sandbox AI usage as policy evolves.
    • Executive-ready reporting that supports audit, legal, and procurement decisions.
    • Playbooks for exception handling, deprovisioning, and user education.

Building Security In through AI


Put security standards, remediation guidance, architecture patterns, and sign-off guardrails “in the box” where work happens. Whether you run in-house LLMs, frontier models, or both, we seed your platforms and agent pipelines with Aedify’s licensable, actively maintained promptbook IP (developed with Manicode.ai) to help developers, architects, and approvers move faster with higher assurance.

  • Developer Agents: standards, remediation, and tests—inside the IDE
    • In the IDE by default making standards useful.
      • Encode your secure coding standards so they’re applied contextually and consistently—without bespoke rule-writing for every technology or environment.
    • Learning from your codebase.
      • Context that adapts to each organization's non-functional security requirements: stack expectations, good/bad practices, and secret handling adjacencies.
    • Find and fix in-flow.
      • Detect code “points of interest” that may indicate risky patterns for developer remediation;
      • Detect and report "points of interest" representing potential insider threat to governance executives directly; and 
      • Propose targeted remediations while the module is open.
    • Test generation and validation.
      • Generate security-related unit/integration tests and resilience checks aligned to your architecture and standards.
  • Architect Agents: automated threat modeling and secure design
    • Codify hard-won architectural lessons.
      • We package architecture-level attack knowledge and guardrails so they’re applied consistently across apps, APIs, cloud, and CI/CD.
    • Zero Regressions for design.
      • As systems evolve, the agent flags deviations, missing controls, and drift from approved patterns—so design mistakes don’t return.
    • Traceable decisions.
      • Produce decision records, reference patterns, and mitigation mappings that engineering can ship.
  • Sign-off Agents: 24x7 guardrails for code promotion
    • Policy-aware, context-sensitive gating.
      • Beyond binary gates, the agent evaluates “unusualness” (timing, frequency, size, author, delta magnitude) and flags promotions that merit human review.
    • Always current with your governance.
      • The LLM tracks changes in policy, standards, risk tolerance, and exception processes—keeping approvals aligned to risk mandates.
  • What You Get
    • IDE-integrated agents for standards application, remediation, and test generation.
    • Architecture agents that automate threat modeling, enforce patterns, and prevent regressions.
    • Promotion guardrails that reduce noisy approvals and spotlight true risk.
    • Artifacts that satisfy audit/GRC while accelerating engineering.

AI Security (Build Secure AI, Test AI Security)

Whether you build, customize, or buy AI systems, we help you design and validate security posture across models, data, and integration surfaces.
  • How It Works
    • Secure-by-design for AI systems.
      • Data governance, identity boundaries, secret management, RAG/data access controls, and tenancy isolation.
    • Adversarial and misuse resistance.
      • Prompt injection/threat modeling, input/output validation, model abuse scenarios, and environment hardening.
    • Supply chain and deployment integrity.
      • Model provenance, artifact attestation, SBOMs, and secure CI/CD for model and agent updates.
    • Operational assurance.
      • Logging, monitoring, and detection for model usage anomalies and data exfil attempts; runbooks for containment and recovery.
  • What You Get
    • Architecture views with trust boundaries and control points for AI workflows.
    • Test plans and evidence for adversarial robustness and governance alignment.
    • Implementation-ready guardrails and pipelines to keep models and data safe in production.

Assessment and Executive Briefing

A top-down diagnostic of your AI posture—usage, build-in capabilities, and AI system security—culminating in an executive briefing that ties risk, throughput, and governance together.
  • How It Works
    • Interview leaders across engineering, platform, product, security, and compliance.
    • Review artifacts that reflect real decisions and change (PRs, promotion requests, incidents, exceptions).
    • Synthesize a narrative: where AI accelerates, where it creates drag, and how to calibrate risk vs. speed.
    • Present to your leadership team—or equip you to do so—with options and ownership.
  • What You Get
    • Executive slide deck with annotated findings and options.
    • Written summary suitable for board, audit, or stakeholder distribution.
    • Optional workshop to align on next steps, owners, and timelines.

Gap Analysis & Acceleration Plan

A two-phase engagement to identify where AI can meaningfully reduce friction and risk—and to sequence a pragmatic rollout that lands in code, process, and platforms.
  • How It Works
    • Gap Phase: assess AI usage/governance, developer and architect workflows, promotion gates, and AI system security against your objectives.
    • Identify underused platform controls and duplicative effort across AppSec, IAM, platform, and developer tooling.
    • MAP Phase: co-develop a prioritized roadmap that sequences capability deployment and policy updates your teams can actually execute.
  • What You Get
    • Written Gap Analysis with clear problem statements and decision impact.
    • Delivery-aware roadmap aligned to product/platform milestones.
    • Briefing language suitable for board or audit contexts.

Tools and Integration

We work with all the enterprise-grade and open-source development tool chains and integrate into the systems you already rely on—IDEs, source control, CI/CD, ticketing, wikis, and monitoring—so AI assistance and governance are visible and enforceable where engineers live.