FalconryX doesn't sit beside your GRC program — it runs inside it. It continuously screens live signals — vendor assessments, KRI readings, control gaps — and triggers a smart action the moment a threshold is crossed. A Natural Language Processing (NLP) engine reads every regulatory update the moment it's published, and the FalconryX Copilot answers questions anywhere in the platform with citations to your actual data. Every recommendation is explainable. Every action has a human approval gate. Intelligence, not autopilot.
Central banks, data & cyber authorities, securities regulators GCC-wide
Across all 5 pillars — see the library below
GOVERN, ANTICIPATE, COMPLY, WITHSTAND, ASSURE
Of consequential actions reviewed before they execute
FalconryX isn't a single feature — it's five distinct AI capability types, each purpose-built for a different part of the GRC workflow and working from the same live data. Specific use cases — including third-party and vendor risk — live inside these engines; the full library is below.
Continuously screens live risk signals — KRI readings, control gaps, and third-party/vendor assessments — and triggers a smart action the moment a threshold is crossed.
Uses Natural Language Processing (NLP) to continuously read regulatory publications across every in-scope jurisdiction, extract new obligations, and map them to your existing controls automatically.
A conversational assistant available across every pillar — not just compliance — that answers questions about your risk, controls, obligations, and audit data, with citations to the actual records behind every answer.
Machine learning models detect control drift between what your policy says and what's actually happening — flagged the moment behaviour deviates from baseline.
Specialised agents — one per task — coordinate evidence collection, gap analysis, and remediation routing, with every consequential step gated for human sign-off.
Not a roadmap — these are live capabilities, organised by the pillar they operate in. Every one of them is grounded in your actual data, with a human gate before anything consequential happens.
Generates a first-pass policy directly from regulatory source text, pre-formatted to your house style — a starting draft, not a blank page.
Surfaces which strategic objectives are threatened by current risk exposure — before the quarterly review, not during it.
Flags unusual conflict-of-interest disclosure timing, frequency, or relationship clustering for governance review.
Scores and monitors every AI system in use across the organisation against your AI governance framework — FalconryX included.
Continuously screens every monitored KRI against its threshold and triggers a smart action — owner notified, review opened — the moment one is crossed.
Continuously scans external sources — news, threat intel, market data — for early signals of risks not yet logged in your register.
Generates an initial third-party risk score from public records, certifications, and historical performance the moment a vendor is added — and rescreens on a defined cycle.
Detects near-duplicate or overlapping risk entries across business units and recommends consolidation before the register fragments.
Reads every regulatory update continuously and maps its impact to obligations, policies, and controls — automatically, before the team finishes reading the circular.
Recommends which existing control most likely satisfies a new framework requirement, scored by mapping strength against similar past mappings.
Pulls compliance evidence directly from connected systems on a defined schedule — no manual screenshot-and-upload cycle before every exam.
Forecasts your readiness score for an upcoming examination based on current evidence completeness and patterns from prior exam cycles.
Builds realistic disruption scenarios from real threat intelligence and your own incident history — ready to run as a tabletop or live exercise.
Suggests likely technology and vendor dependencies for a critical service, based on patterns from similar services already mapped.
Drafts stakeholder notifications — board, regulator, customer — the moment an incident is classified, ready for human approval and send.
Identifies recurring lessons-learned themes across multiple exercises to flag systemic gaps in your response capability, not just one-off misses.
Drafts audit programmes and suggests risk-based sample sizes directly from the engagement scope — a starting point for the auditor, not a replacement.
Flags anomalies in a test population automatically — before the auditor manually reviews every line of a 200-item sample.
Matches new findings against historical root causes to surface systemic control failures — the pattern the audit committee actually needs to see.
Generates the committee narrative directly from live engagement and findings data — a reviewable first draft, not a blank page the night before.
Pulls from every connected module — risk register, obligations, controls, incidents, vendor assessments plus external regulatory sources.
Natural Language Processing (NLP) models parse unstructured text — regulations, contracts, policies — into structured, mappable data points.
Models score severity and proximity to threshold — surfacing what needs attention now, ranked by how close it is to becoming a problem.
Generates a specific, explainable recommendation — with the reasoning and source data any reviewer can trace and challenge.
Routes the recommendation to the accountable human for sign-off before anything changes in your environment. No silent automation.
Every engine from the overview above, shown working. Click a tab to switch — each one replaces the view below, the same way it works inside the product.
FalconryX continuously screens every monitored KRI, vendor assessment, and control gap against its threshold — and triggers a smart action the instant one is crossed, with the reasoning shown alongside it.
Ask a question in plain language, from anywhere in the platform — not just COMPLY. Get an answer grounded in your live data, with the exact records cited, not a generic response from a general-purpose model.
Using Natural Language Processing (NLP), FalconryX reads regulatory text the moment it's published, extracts new or amended obligations, and maps them to existing controls and policies — before your compliance team finishes reading the email.
FalconryX compares what each policy requires against what's actually happening in the connected system — and flags the gap the moment behaviour drifts from baseline, not at the next scheduled test.
A task too broad for one model is broken into steps, each handled by a specialised agent — with a human approval gate before the final, consequential step executes.
In a regulated environment, an AI recommendation is only as useful as your ability to explain it. The six pillars below are structured around the core management areas of ISO/IEC 42001 the international standard for AI management systems so FalconryX's governance approach maps directly to it, area by area.
Every prediction, score, and recommendation comes with the underlying data and reasoning not a black-box confidence number. Reviewers can trace any output back to source.
FalconryX recommends; it does not act alone. Every consequential action a sent communication, a control update, a closed finding — requires explicit human sign-off.
Your data trains and informs only your instance — never pooled across customers. Regional hosting options keep data within GCC jurisdictional boundaries where required.
Every model output, every human decision to accept, modify, or reject a recommendation — logged immutably. Ready to produce the moment a regulator or auditor asks.
Each model in FalconryX — including FalconryX itself — is registered, risk-assessed before deployment, and reviewed on an ongoing basis through Falconry360's own AI Governance module.
Models are reviewed and retuned against confirmed outcomes — including dismissed false positives — to cut noise over time, so alerts stay credible and your team doesn't learn to ignore them.
