Life Sciences AI
AI Drug Safety Signal Detection: Governing Models in Pharmacovigilance
AI can help surface possible safety signals from large volumes of adverse event reports. A signal is only a starting point for medical review, so the model that produces it needs validation, oversight, and records an inspector can follow. Kriv AI helps pharmacovigilance teams build that governance.
AI drug safety signal detection uses models to find possible new adverse events in spontaneous reports, trial data, and literature. A signal is a hypothesis, not proof of harm, so governance must cover validation, human medical review, and audit records. Kriv AI provides this governance work for pharmacovigilance teams, starting at $200 per hour.
context
What a Safety Signal Is, and What It Is Not
Signal detection sits at the front of drug safety work. Any model that ranks or flags adverse event patterns is shaping which questions medical reviewers ask first.
The regulatory definition
The European Medicines Agency defines it plainly: "A safety signal is information on a new or known adverse event that may be caused by a medicine and requires further investigation." EMA adds that safety signals can be detected from "a wide range of sources", such as spontaneous reports, clinical studies and scientific literature.
EMA also draws the line that matters for any AI system: "The presence of a safety signal does not directly mean that a medicine has caused the reported adverse event." Assessing a signal is the step that establishes whether a causal relationship exists, and that step stays with qualified people.
Where the data comes from, and its limits
FDA describes its adverse event database as "designed to support the FDA's post-marketing safety surveillance program for drug and therapeutic biologic products." FDA is also consolidating its reporting systems, and the page now carries the name FDA Adverse Event Monitoring System (AEMS), formerly FAERS.
FDA is direct about the limits of this data: "this does not mean that the drug or biologic caused the adverse event." It also notes that "Duplicate and incomplete reports are in the system." Any detection model trained or run on this kind of data inherits those problems.
ai gap
Where AI Signal Detection Goes Wrong
The common failures are governance failures, not exotic algorithm failures. A model tuned to reduce reviewer workload can quietly suppress rare but serious events. Duplicate reports can inflate a pattern into an apparent signal. Changes in coding, reporting behavior, or product use can shift the data under a model that nobody is re-testing. A vendor can update the model between audits without notice. And when a reviewer cannot see why a case was flagged or not flagged, the team cannot defend the decision to an inspector.
Each of these is avoidable with ownership, validation evidence, monitoring, and a documented human decision at the point where a signal is accepted or closed.
capabilities
What Good Governance of Signal Detection Includes
A defined intended use and named owner
State exactly what the model does: prioritizes cases, groups duplicates, suggests drug-event pairs, or drafts narratives. Name a business owner in safety and a technical owner, and record which decisions the model may inform and which it may not make.
Validation against known signals and known noise
Test whether the model finds signals your team already confirmed, and whether it flags patterns that medical review later rejected. Record sensitivity for rare serious events separately from overall accuracy, and keep the test sets and results as controlled documents.
Human medical review as a gate
Make qualified medical review an explicit step before any signal is validated or closed. Log the reviewer, the decision, and the reasoning, so the record shows a person assessed causality rather than a model score.
Monitoring, change control, and audit trail
Track data quality, duplicate rates, and model performance over time, with thresholds that trigger review. Treat model or vendor updates as controlled changes, and keep versioned logs that let you reconstruct what the model saw and returned for any past case.
framework
Using the NIST AI RMF as the Backbone
NIST describes the AI Risk Management Framework as "intended for voluntary use and to improve the ability to incorporate trustworthiness considerations into the design, development, use, and evaluation of AI products, services, and systems." It is not specific to pharmacovigilance and sets no regulatory requirement, but it gives a neutral structure for the steps above: map where AI is used, measure its performance and risk, manage it with owners and controls, and govern the whole with accountable roles. Your quality system and applicable regulations still define what must be validated and retained.
differentiation
How This Differs From Our Other Life Sciences Pages
This page is about governing AI that detects possible safety signals. Our pharmacovigilance audit page covers why such systems get flagged during inspections, our 21 CFR Part 11 and GxP page covers computer system validation, our FDA model credibility page covers AI used in drug submissions, and our life sciences governance page covers the full service.
engagement
How an Engagement Works
We start by inventorying the AI used anywhere in case intake, triage, signal detection, and narrative drafting, and rank each by the consequence of a missed or false signal. We then review ownership, validation evidence, human review gates, monitoring, and vendor change control for the highest-impact models, and deliver a documented gap list and remediation plan your safety, quality, and IT leaders can act on. We work alongside your teams and do not resell any safety software.
tiers
What You Get at Each Tier
1. Enterprise / life sciences (pharma, biotech, CROs)
A model inventory, risk ranking, validation and monitoring plan, and governance documentation for the AI used in safety surveillance.
2. Fractional CTO / AI governance lead
Ongoing oversight as models, vendors, and data sources change, including review of vendor model updates before production use.
3. Specialized advisory
A single session or second opinion on a signal detection approach, a validation protocol, or a vendor claim under evaluation.
rate card
Kriv AI's Rates for This Work
These are Kriv AI's own published rate floors, not an industry average.
| Track | Kriv hourly rate | Typical engagement model | Minimum engagement |
|---|---|---|---|
| Enterprise / regulated (banks, broker-dealers, payment processors) | From $200/hr | Fixed-scope project or retainer | $8,000 |
| Fractional CTO / AI governance lead | $300 to $400/hr | Part-time, ongoing (monthly) | $8,000 |
| Specialized advisory (vendor evaluation, second opinion) | $400 to $700/hr | Hourly, per-session | Varies by engagement |
| Small business | $150/hr | Referred to Kriv AI's partner network | n/a |
get a quote
How to Get a Real Quote
The rates above are floors, not a quote. Actual price depends on how many safety-surveillance models and data feeds are in scope, how much validation and monitoring documentation already exists, and which regulators you report to. Book a discovery call and we will scope it honestly.
Straight answers
Frequently asked questions about AI Drug Safety Signal Detection: Governing Models in Pharmacovigilance
What is AI drug safety signal detection?
It is the use of models to find possible new or changed adverse event patterns in sources such as spontaneous reports, clinical studies, and literature. The output is a candidate signal that qualified reviewers must assess, not a conclusion that a drug caused harm.
Does a safety signal mean the medicine caused the event?
No. EMA states that the presence of a safety signal does not directly mean the medicine caused the reported adverse event, and FDA says the same of reports in its database. Assessment establishes whether a causal relationship exists.
What are the main data-quality risks?
FDA notes that its adverse event data contains duplicate and incomplete reports and that the data by themselves are not an indicator of a product's safety profile. Models built on such data need duplicate handling, validation, and monitoring.
Do you sell a pharmacovigilance or signal detection product?
No. Kriv AI provides governance, validation, and vendor-assessment consulting and does not resell any safety software.
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