Advisory services
Each service is designed for leaders who need accountable decisions, implementable controls, and a usable route through evolving AI risk and regulation.
Vendor-agnostic · Principal-ledCore advisory
Every engagement can start here—readiness, assessment, agent governance, or ongoing advisory—then extend into the specialized work below as your AI program matures.
Turn an uncertain regulatory landscape into a clear program of work. We map the AI systems you operate to relevant responsibilities and prioritize the controls, evidence, and decisions required.
AI risk lives across models, prompts, data, identities, APIs, cloud infrastructure, third-party components, and human operating processes. The assessment follows those real paths.
When AI agents can access tools, data, and workflows, governance needs to move from policy statements to actionable guardrails, approvals, and audit trails.
Use ongoing advisory when a point-in-time assessment is not enough. The focus is strategic direction, architectural decisions, and practical governance that evolves with your AI program.
Securing the intelligence layer, end to end
As AI moves from answering questions to taking actions, the attack surface and the accountability gap both grow. These specialized engagements meet the risks that generic security programs miss—each delivered on our Discover / Assess / Prioritize / Enable model, with reviewable evidence at every step.
Prove how your AI behaves under a determined adversary—indirect prompt injection, poisoned context, and abuse of autonomous tool use.
Autonomous agents read untrusted content and call real tools. A single injected instruction in a document, email, or web page can turn a helpful agent into an insider threat—exfiltrating data, invoking privileged actions, or chaining tools in ways no one tested.
A structured adversarial engagement that treats your agent as a live target: we craft real injection and tool-abuse scenarios against your actual workflows, not a checklist, and show exactly where guardrails hold and where they fail.
Inventory every third-party model, API, and embedded LLM in use—including the ones no one told security about.
Teams adopt LLM features, copilots, and vendor AI faster than governance can track. Each is a data-exfiltration path and a supply-chain dependency you have not vetted—until an audit, a breach, or a regulator asks what you are running.
A discovery engagement that surfaces sanctioned and shadow AI across the organization, maps the third-party dependency chain behind each, and turns an unknown estate into a governed register with clear owners and risk ratings.
Design the approval gates, intervention points, and escalation paths that keep autonomous agents accountable.
“Human oversight” is easy to claim and hard to operate. Without explicit gates, agents either act on high-consequence decisions unchecked, or humans rubber-stamp everything and oversight becomes theater—neither survives a board or regulator’s question.
A practical HITL framework that classifies actions by consequence, defines exactly where a human must approve, intervene, or be notified, and makes each decision traceable—so oversight is real, proportionate, and auditable.
Technical guardrails and LLM firewalls that enforce your rules at runtime—not just on paper.
Written AI policies do not stop a prompt, a data leak, or a non-compliant output. Between the policy PDF and the running system sits a gap where real incidents happen—and manual review does not scale to machine speed.
We translate your policies into enforceable technical controls—input/output filtering, an LLM firewall layer, allow/deny tool policies, and rate and scope limits—so violations are blocked or flagged automatically, with evidence.
Response playbooks for the failures classic IR never covered—model drift, hallucination, prompt-injection compromise, and unsafe autonomous action.
Traditional incident response assumes a breach of systems, not a betrayal of judgment. When a model hallucinates into a customer decision, drifts out of tolerance, or is hijacked through injected instructions, most teams have no defined detection, containment, or communication path.
An AI-specific IR program that defines what counts as an AI incident, how to detect and contain it (including kill-switch and rollback for agents), and who communicates what to leadership, customers, and regulators—rehearsed, not theoretical.
Discuss an advanced engagement Try the AI Act Risk Classifier
The engagement process
Every engagement follows the same transparent arc, so you always know where you are and what comes next.
A confidential conversation to understand your AI estate, obligations, and the decisions ahead.
Agree scope, then inventory systems, owners, data flows, and autonomy levels.
Evaluate security and governance gaps and classify systems against real risk.
Deliver a prioritized roadmap with accountable owners and decision points.
Support implementation and review progress—continuously or at defined milestones.
Engagement model
Most organizations begin with a defined scope. That creates an evidence-backed baseline before deciding whether they need a deeper assessment or retained advisory.
We will discuss your context before recommending any engagement.