Solutions by role / CISO and Security
AI for CISOs: move faster without losing control
Banning every AI tool is no longer realistic. Security needs to see what is in use, limit data and permissions, and prove that controls work in the real workflow.
- A visible AI inventory
- Least privilege and action logs
- Human approval for high-impact actions
01 / The problem
Where time, money and control are being lost
This is not a list of fashionable tools. These are operating problems we can test against your data and measure before development starts.
Shadow AI is already inside the business
Employees send work data to public services and security learns about it only after a concern appears.
Explore this problem 02Agents have wider permissions than the task needs
A system can read, change or send more information than required for its narrow purpose.
Explore this problem 03Audit evidence is missing
A policy exists, but no one can reconstruct what the agent saw, why it acted or who approved the outcome.
Explore this problem 04The team is buried in alerts
Routine enrichment and context gathering consume the time needed for genuinely dangerous events.
Explore this problem02 / In depth
What each problem looks like in practice
The short cards above are navigation. Each problem below is explained through business impact, a practical operating change, a measurable outcome and the questions leaders usually need answered before a pilot.
Shadow AI is already inside the business
Employees send work data to public services and security learns about it only after a concern appears.
Why it becomes expensive
Employees already use external models through personal accounts, extensions and unapproved services. The company cannot see what data is shared, where it is retained or who owns the outcome. A ban without a usable alternative only makes usage less visible.
What changes in the workflow
We build an inventory of actual use: user, process, data class, provider, external actions and owner. Low-risk cases get a simple approved route; sensitive scenarios move into a governed boundary or stop until the risk is corrected.
What a verifiable result looks like
The CISO gains visibility instead of assumptions. Every use case has an owner and disposition: allow, restrict, migrate or stop. Staff know which route is approved and how to propose a new scenario.
Practical questions
Should public AI services be banned completely?
A total ban rarely solves the problem. Separate data and workflows by risk, offer a safe alternative and place stronger controls around high-risk use.
How do we discover tools already in use?
Combine process-owner interviews, SSO and expense evidence, network visibility and browser-extension review within the company’s authority.
Agents have wider permissions than the task needs
A system can read, change or send more information than required for its narrow purpose.
Why it becomes expensive
An agent with broad access to email, files, CRM and external actions can turn one error or malicious instruction into a large incident. A shared service account also hides who initiated the action and why.
What changes in the workflow
Grant minimum permissions for a specific workflow, separate reading from writing, and limit tools and action volume. Dangerous operations require approval, while user and agent identity remain attached to every step.
What a verifiable result looks like
Compromise of one workflow does not expose the full environment. Audit evidence shows the initiator, permission, data and approval. Access can be revoked quickly, and scope changes trigger a fresh review.
Practical questions
Is one technical account sufficient?
No, not when different users and workflows share it. Actions need a traceable user identity, role and reason for access.
Which actions require approval?
External sending, record changes, deletion, payments and access grants are common examples. The exact list follows process risk and company policy.
Audit evidence is missing
A policy exists, but no one can reconstruct what the agent saw, why it acted or who approved the outcome.
Why it becomes expensive
Without complete traces, investigators cannot reconstruct which document an agent saw, why it selected an action or who approved it. Incident reviews rely on guesses, while audits receive screenshots instead of reproducible control evidence.
What changes in the workflow
We define the required trace: model and instruction version, sources, permissions, tool calls, evaluation results, human decision and final action. Logs are protected, retained for a defined period and tied to the workflow identity.
What a verifiable result looks like
An incident can be replayed step by step, and control can be sampled without manual archaeology. The team can distinguish a model error from a data, permission, prompt, integration or review failure.
Practical questions
Must every full prompt be retained?
Not always. The log design depends on sensitivity and retention rules. Preserve enough evidence to reproduce behavior without creating a new repository of secrets.
Who should access the logs?
Only roles that need them for operations, security or audit. Log access and export should themselves be recorded.
The team is buried in alerts
Routine enrichment and context gathering consume the time needed for genuinely dangerous events.
Why it becomes expensive
AI can produce many weak warnings and long summaries that do not support a decision. Analysts spend time rebuilding context, queues grow, and a genuinely dangerous case waits alongside noise.
What changes in the workflow
We use AI to collect and structure evidence, not to make unconstrained incident decisions. Historical events test completeness, cited facts and prioritization. High-risk actions stay with deterministic controls and analysts.
What a verifiable result looks like
The analyst receives a compact packet: what happened, affected assets, supporting events and the next verification step. Value is measured through context-gathering time, priority quality and missed critical cases.
Practical questions
Can AI close alerts automatically?
Only for narrow, well-tested classes with safe recovery. Elsewhere it prepares context and a recommendation while an analyst or deterministic rule decides.
How should a SOC assistant be evaluated?
Use historical incidents, false positives and deliberately difficult cases. Tests must score evidence links and dangerous omissions, not just fluent summaries.
03 / KPI
What we measure before a pilot
A result needs a baseline. We record the current cost, speed and quality first, then compare the pilot with the same work.
- AI inventory coverage
- Critical access findings
- Detection and response time
- Evidence and log completeness
04 / Workflows
What can change in day-to-day work
Every workflow has a clear action, a system boundary and a human decision point. You know what is automated and who remains accountable.
AI inventory
Find services, agents, models, data sources, owners and external dependencies.
Control: The CISO approves permitted use and remediation priority.AI agent audit
Test instructions, data, permissions, harmful paths, logging and stop behavior.
Control: Findings are reproduced and moved into an owned remediation plan.Private AI boundary
Deploy where data and model handling match the organization’s requirements.
Control: Role-based access, actions and changes remain auditable.SOC analyst assistant
Collect event context and similar cases, then prepare an investigation draft.
Control: Blocking or containment follows an approved rule or a human decision.The World Economic Forum highlights AI-related vulnerabilities, data leakage, skill shortages and weak human oversight as practical cybersecurity concerns. Source.
AI is expanding the attack surface while also giving security teams new operating leverage.
05 / Delivery
From one useful workflow to a working system
We do not redesign the company around a pilot. We test one bounded workflow, prove the economics and expand only when the evidence is good.
Diagnose
Choose the process, owner, data and constraints.
Baseline
Record current cost, time, errors and risk.
Pilot
Test a bounded slice of real work with real users.
Integrate
Connect systems, permissions, logs and approvals.
Decide
Scale, revise or stop based on measured results.
06 / Next
Related services
Questions to answer before you start
How is an AI agent audit different from a penetration test?
It covers not only the technical perimeter but instructions, data, action permissions, decision quality, logs and response to hostile input.
Can data stay entirely inside our environment?
Yes, for suitable use cases. A private boundary still needs access control, logging and source governance.
Does a person need to approve every action?
No. Approval should match consequence. Safe and reversible tasks can run automatically within tested limits.
What does the audit deliver?
A component and data inventory, tested risk scenarios, findings, priorities and a remediation plan with owners.
Discuss your workflow
Discuss my workflow
Show us one AI workflow or agent. We will examine its data, permissions, logs and the points that need mandatory human control.