AI introduces a new attack surface — prompt injection, data leakage, agent overreach, vendor risk. Your fractional Chief AI Officer designs security in from day one, grounded in OWASP, NIST, and MITRE — and gives you the review-ready evidence you can hand to your board.
A new attack surface
AI risk is probabilistic, leaky, and accessible. Anyone with a text input can probe the model. PII can leak via prompts. Agents can overreach when tool access is too broad. Vendor data and retention settings can differ by product tier. And hallucinations get committed by humans assuming the AI was right. Traditional perimeter thinking doesn’t meet this risk — the threat enters through the same interface your customers use.
The SMB exposure
For SMBs the exposure is asymmetric: the same regulatory expectations as enterprise, a fraction of the security headcount. The answer is not bolting on security after launch. The answer is designing security in from day one and using recognized standards as the evidence model.
Defense in depth reduces the chance that one control failure becomes a system-wide failure. We design workflows so sensitive data, model behavior, tool access, and operational response each have their own control layer.
Layer 01
What enters the system
Protects against
PII leakage, secret exposure, prompt-laundered data exfiltration. The first opportunity for sensitive data to leave your boundary is the moment a user types it into a prompt — that’s where you stop it.
Controls in practice
Layer 02
Provider selection and configuration
Protects against
Vendor data retention abuse, training on your data, residency violations. The provider you choose — and how it’s configured — determines who else can ever see, learn from, or be subpoenaed for your data.
Controls in practice
Layer 03
Guardrails in the product
Protects against
Prompt injection, jailbreaks, sensitive output leakage, unbounded cost. The application is where adversaries meet your model — and where most real-world attacks land. This is your front line.
Controls in practice
Layer 04
When AI takes actions
Protects against
Excessive agency, runaway tool calls, unintended state changes. The moment the model can act on the world, the blast radius of a single bad decision multiplies — this layer keeps that radius bounded.
Controls in practice
Layer 05
Visibility and response
Protects against
Silent failures, undetected drift, slow incident response. The controls above only hold if you can see them holding — this layer keeps the lights on and the system honest over time.
Controls in practice
Five layers, defense in depth. One control layer failing should not mean the whole workflow fails.
Every deployment is anchored to industry frameworks your auditors, insurers, and enterprise customers already recognize. That’s how a security decision becomes a defensible one.
2025 edition
A widely used industry checklist for LLM application threats: prompt injection, sensitive information disclosure, supply chain risk, excessive agency, system prompt leakage, vector and embedding weaknesses, misinformation, unbounded consumption, and more. Maintained by a global community of security practitioners.
How we use it
Every deployment is mapped against the Top 10 before launch. Each item is either mitigated with a documented control or formally accepted with an owner and review date.
Govern · Map · Measure · Manage
A voluntary U.S. government framework for managing AI risks and improving trustworthy AI practices. It provides a structured way to think about AI risk across governance, mapping, measurement, and management activities.
How we use it
We translate NIST AI RMF into your environment: a one-page AI policy, a threat map, a KPI dashboard, and an incident playbook — sized for an SMB, not a Fortune 500.
Adversarial Threat Landscape for AI Systems
The MITRE-maintained catalog of known AI attack tactics, techniques, and case studies. The ATT&CK matrix, but for adversarial machine learning — prompt injection, model evasion, training-data poisoning, model theft, and more.
How we use it
Our quarterly red-team exercises probe your deployments against ATLAS techniques. Findings become prioritized remediation items with owners and target dates.
OpenAI Business · Anthropic Enterprise · Vertex · Azure OpenAI
Enterprise AI providers publish product-specific privacy, security, and compliance terms. Depending on the provider and plan, those terms may include training opt-outs, configurable retention, BAA availability, regional controls, encryption options, SOC 2 reports, or ISO 27001 certificates.
How we use it
We compare vendors against your regulatory profile, configure the strongest applicable settings available on the chosen tier, and keep a living vendor risk register so material policy changes are reviewed.
Frontier safety frameworks
Responsible Scaling Policies and Preparedness frameworks — published by model providers themselves — describe internal evaluation and capability-threshold approaches for powerful systems. SMB deployments can borrow the pattern: evaluate before launch, stage rollout, and define when to pause.
How we use it
When we ship agentic systems, we follow the same internal-evaluation patterns: capability evals, pre-launch red-teaming, staged rollout, and explicit deprecation criteria.
Trust services criteria & ISMS controls
The control families your auditors already know — access management, change management, monitoring, incident response, vendor management — apply directly to AI workloads. Mapping into them keeps your AI evidence inside your existing compliance program, not adjacent to it.
How we use it
We design AI-specific evidence to drop into your existing SOC 2 / ISO 27001 control set: model inventory, prompt change management, output monitoring, vendor reviews.
The frameworks tell you what to defend. The guardrails are how we actually defend it — concrete, code-level patterns that ship with protected AI workflows we deploy.
A pre-flight filter scrubs identifiers before any prompt leaves your network. Names, account numbers, PHI markers, and other classified fields are redacted, replaced with pseudonyms, or blocked entirely.
Pattern
Regex + ML-based detectors running at the gateway, with policy per workflow and per data classification.
Value
Reduces accidental data sharing with model providers when an employee pastes the wrong thing into the wrong window.
System prompts and user input are kept structurally separate so user content is less able to rewrite instructions. The implementation avoids relying on a single flattened string and uses explicitly typed roles.
Pattern
Parameterized templates with a strict {user_input} slot, role-aware messages, and escape rules for embedded text.
Value
Reduces prompt-injection risk and makes the interaction easier to inspect in the audit log.
Responses in protected workflows are screened for secrets, keys, PII, and policy-forbidden content before they reach the user, downstream system, or log.
Pattern
Regex sweeps plus a policy classifier, with severity tiers and configurable actions: redact, block, or escalate.
Value
Catches leakage even when input controls missed something — a second line of defense on the way out.
Agents can only call APIs, tools, files, and resources explicitly granted to them. Everything else is rejected and logged as an anomaly.
Pattern
A broker enforces the allowlist on tool calls; service accounts are workflow-scoped where feasible.
Value
Limits excessive agency by making unauthorized tool use fail closed and appear in logs.
Per-user token caps, per-workflow concurrency limits, and hard monthly spend ceilings. Anomalies and budget thresholds wake the right person in real time.
Pattern
Gateway-level throttling with alerts firing at 60% / 80% / 100% of budget, plus rate-of-change detection.
Value
Protects against runaway cost, scripted abuse, and the slow-burn breach that only shows up in next month’s bill.
Inputs, outputs, model versions, decisions, and tool calls are recorded where the workflow risk requires it. The log is designed as operating evidence, not a forensic afterthought.
Pattern
Append-only logs with cryptographic chaining, retention policies matched to your regulator, and exportable on demand.
Value
Lets you replay, audit, and prove what happened — to your team, your customers, or your regulators.
One workflow at a time. Scoped durations, clear deliverables, and a feedback loop that hardens the system every quarter — not just the day it ships.
01
1–2 days
Deliverable
One-page threat map with prioritized risk ratings
02
2–5 days
Deliverable
Control catalogue + vendor checklist
03
1–3 weeks per workflow
Deliverable
Deployed workflow with security designed in
04
1–3 days per workflow
Deliverable
Red-team report with prioritized remediation log
05
Ongoing
Deliverable
Live dashboard + 90-day review cadence
This is the methodology behind every AI workflow we ship.
This is what an evidence package looks like when security is designed in from day one. Protected workflows ship with evidence you can share with your board, customers, counsel, or auditors — without scrambling to reconstruct decisions later.
A one-page policy tying your AI usage to OWASP, NIST AI RMF, and your sector regulations. Reviewed by leadership and ready to support RFP responses or audit requests.
Protected workflows mapped to known threats, with mitigation status, owner, residual risk rating, and next review date. A living document for audit or insurance review.
BAAs where applicable, DPAs where needed, SOC 2 reports, ISO 27001 certificates, retention-control confirmations, and subprocessor lists for providers in your data path — collected, indexed, and refreshed on a regular cadence.
Data flow diagrams showing where customer data goes, who has access at each hop, which controls apply, and where the boundaries live. The map an auditor or new engineer can read.
The log schema, redaction rules, retention policy, and a sample export showing what a regulator or auditor would see when they ask. Tamper-evidence designed into the workflow.
Executive summary, technique catalog (OWASP + MITRE ATLAS), findings, and remediation log from each exercise. Evidence for how controls performed under pressure.
Roles, escalation paths, vendor contacts, regulator notification timelines, and SLAs — tested in tabletop exercises so the team isn’t learning it on the worst day of the quarter.
A standing document showing what controls were tested, what changed, what the metrics did, and what’s planned next quarter. The cadence that turns security into a program, not a project.
Security designed in. Built for audit review. Evidence ready when anyone asks — your board, your largest customer, or an auditor asking for support.
Explore deeper
Three more deep dives on how a fractional Chief AI Officer changes the math — and the risk posture — for small and medium businesses.
Reduce costs
Customer support, back-office automation, content production, lead qualification — every line item in your P&L has a cost-cut waiting.
Read the deep diveGrow faster
AI does not replace your people. It reduces repetitive prep, routing, drafting, and coordination across sales, marketing, ops, and engineering.
Read the deep diveStay compliant
HIPAA, SOC 2, GDPR, audit trails, kill switches, and role-based access considered before workflows reach production.
Read the deep diveYour fractional Chief AI Officer designs security in from day one — frameworks, guardrails, audit trails, and the artifacts to prove it. We won’t ship a workflow that can’t pass an audit. Without a security consultancy on retainer.