
Last reviewed: September 2026 · 19 minute read
Adverse media screening is the process of checking a person or company against databases of negative news and public records to flag financial crime, corruption, sanctions or reputational risk. A clean adverse media screening result means a name did not match the database. It does not mean the third party is clean. Five structural blind spots sit behind that result, and closing them takes a second layer: open-source, multi-language investigation with human adjudication.
Adverse media screening, also called negative news screening or an adverse media check, is the practice of checking a customer, supplier, investment target or other third party against collections of negative news and public records. The goal is to surface links to financial crime, fraud, corruption, sanctions evasion, terrorism financing, environmental harm or reputational damage before you enter or continue a relationship with that party.
A news report that a company director was charged with fraud is adverse media. So is an NGO report linking a supplier to forced labor, a regulator’s enforcement notice, or a court judgment. Any one of these should change how you treat the third party.
Most teams run adverse media screening by searching a name through a commercial database. The tool returns matches, an analyst clears or escalates them, and the case moves forward. It is fast, repeatable and cheap. It is also the most misunderstood control in the due diligence stack, because a clean result feels like proof of safety when it is only proof of no match.
Adverse media screening sits inside the wider discipline of third party due diligence. It is one input to a risk decision. The problem starts when it becomes the only input.
Adverse media screening needs to cover 5 categories of harm: financial crime, sanctions and terrorism, regulatory and legal action, ESG and conduct issues, and reputational or behavioral risk. A database tuned to one category can miss another that is just as damaging.
Adverse media also splits by structure. Structured adverse media is already collected, tagged to an entity and stored in a database, ready to match. Unstructured adverse media is everything else: an investigative article, an NGO field report, a local-court notice, a social post. A database matches the first kind. A large share of real risk lives in the second, and that is the first place adverse media screening goes blind.
Adverse media screening is a point-in-time check, usually at onboarding, that asks whether adverse media exists on a name today. Adverse media monitoring is continuous or scheduled, and asks whether anything new has surfaced since the last check. Getting this distinction right is the difference between a photograph and a film.
| Dimension | Adverse media screening | Adverse media monitoring |
|---|---|---|
| When it runs | Point-in-time, usually at onboarding or periodic review | Continuous or scheduled, across the relationship |
| Question it answers | Is there adverse media on this name today? | Has anything new emerged since we last checked? |
| Blind spot it leaves | Everything that surfaces after the screen runs | Still limited to the sources the tool ingests |
| Best used for | The initial risk decision | Keeping a decision current for higher-risk relationships |
Monitoring closes the time-gap blind spot, but only within the coverage the tool already has. If the underlying sources are English-only and structured, checking them more often does not widen coverage. It refreshes the same narrow view faster. Both controls matter. Neither is enough on its own.
A clean adverse media screening result means a name did not match a curated, pre-collected database. It does not mean no risk exists. A structured database is a filtered snapshot: a vendor collects news, decides what counts as relevant, tags it to an entity, and serves the match. Every step is an editorial call made before you ever ran the search.
One major data vendor states that its adverse media data fits its “highly structured format” and “strict inclusion criteria.” Another admits its standard monitoring covers only a fraction of global media output, and that reputational stories often break first in regional or local-language outlets, days or weeks before English-language wires pick them up.
There is a second problem: noise. According to industry benchmarks reported by LexisNexis Risk Solutions and KPMG, name-based adverse media screening runs at a 90 to 95 percent false-positive rate across most institutions. With global sanctions designations reaching 79,830 by March 2025, alert volume keeps climbing. A team can clear thousands of alerts a month and feel productive while the risk sitting outside the database never gets touched.
The obligation to look predates the tools sold to satisfy it. The Financial Action Task Force’s 40 Recommendations, which most national AML regimes implement, require ongoing due diligence under Recommendation 10, including scrutiny to keep risk understanding current rather than a one-off onboarding check. The Wolfsberg Group treats adverse media screening as a risk-based control, and the EU’s Sixth Anti-Money Laundering Directive widened liability for failures. For an enterprise outside financial services, the same logic arrives through DOJ compliance guidance, forced-labor import law, anti-bribery rules and ESG exposure. You are expected to have looked properly, and to prove it.
Any adverse media screening product that runs against pre-collected, pre-structured data inherits the same 5 blind spots. These are not failures of one vendor. They are properties of the model.
A database can only match what someone already collected and tagged. Court filings that were never digitized, regulatory actions in jurisdictions with weak data infrastructure, and disputes settled privately never enter the index. The absence of a hit is the absence of a record in that vendor’s collection, not the absence of risk.
English is a minority of the world’s news. A comparison of two widely used global news datasets, published on arXiv, found one indexed articles in roughly 64 languages while the other reached only 14, with English making up around 40 to 52 percent even of the broader set. A reputational story about a supplier in SĂŁo Paulo or a distributor in Jakarta often breaks in Portuguese or Bahasa Indonesia and is never translated. Adverse media screening tuned to English-language sources cannot see it, and machine translation of a headline the system never ingested does not help.
Name matching is literal. A subject who operates under a variant spelling, a transliterated name, a maiden name, or through a holding company two layers up will not match a screen run against the name on the contract. Beneficial-ownership opacity is the failure examiners cite most often, and it is exactly the layer a name screen cannot reach. This is why UBO verification belongs in any serious program.
Every adverse media provider publishes inclusion criteria: which sources qualify, what counts as adverse, what gets excluded as unverified. Those are defensible editorial choices, but they are made without reference to your risk appetite. A blog post, an NGO field report, or a local-court notice that would matter to your decision may sit permanently outside the vendor’s definition of includable data.
A screen is a photograph. Risk is a film. A third party onboarded clean in January can be charged, sanctioned or exposed in a July investigation, and a program that screened once at onboarding will not know. DOJ guidance is explicit that third-party management must continue throughout the life cycle of the relationship.
A Neotas analyst reviews a sample of your current adverse media screening against open-source, multi-language investigation on the same subjects, and shows you what a name-only screen missed. Most sessions run 30 minutes, paired with a risk intelligence analyst rather than a sales rep.
Schedule a callUse this map to locate where your current adverse media screening stops and where risk continues. A name-only database screen covers the top row well and the second row partially. Everything below it needs a second layer.
| Where the risk lives | Example signal | Reachable by a database screen? | What it takes to reach it |
|---|---|---|---|
| Structured, English, on-name | Sanctioned entity, US federal indictment | Yes | Standard adverse media screening |
| Structured, non-English | Local regulatory action, foreign court record | Partial | Native-language sources |
| Unstructured, English | Investigative article, NGO report, blog | No | Open-source search and reading |
| Unstructured, non-English | Regional-press exposé, local social media | No | Native-language OSINT plus an analyst |
| Hidden by alias or ownership | UBO 2 layers up, transliterated name | No | Entity resolution and investigation |
| Behavioral or reputational | Online conduct, undisclosed association | No | Analyst adjudication |
Take a third party you are about to onboard and ask, row by row: if the risk lived here, would your current adverse media screening process find it? For a low-risk, domestic, well-documented counterparty, the top rows may be enough. For a cross-border supplier, a high-value acquisition target or a politically exposed counterparty, the risk usually sits in the lower rows, where a screen returns clean.
The gap has a cost attached. UFLPA detentions carry direct costs before any penalty: storage, demurrage, re-routing and lost sales while goods sit at the border. On the enforcement side, DOJ compliance guidance decides whether a company facing a criminal resolution gets credit for an effective program or pays a heavier penalty for a deficient one. A clean adverse media screening result with no investigation trail is the kind of thin record that costs credit at exactly the moment it matters.
Adverse media screening is one workstream inside a wider third party risk management program, and every mature adverse media screening process ties back to it. On its own, it is a data check. Positioned correctly, it is the trigger that decides how deep the rest of the diligence needs to go.
For a full walk-through, the Neotas TPRM lifecycle guide maps where adverse media screening, investigation and monitoring sit across onboarding, review and offboarding, and the enhanced due diligence checklist covers the people, ownership and adverse media workstreams a name screen alone can’t satisfy.
Regulators no longer accept “we ran an adverse media screening check” as the answer. The expectation is that you investigated in proportion to risk, reached a documented decision, and can produce the evidence on request.
The revised guidance directs prosecutors to ask how a company is using available data to evaluate vendor risk during the course of the relationship, and treats flawed or incomplete due diligence as a source of liability. A clean adverse media screening result with no investigation trail is exactly the thin record this guidance is written to catch.
The Uyghur Forced Labor Prevention Act applies a rebuttable presumption: goods linked to the Xinjiang region are presumed made with forced labor and barred, and the importer must prove otherwise. Enforcement is climbing. US Customs and Border Protection detained 6,636 shipments in the first half of 2025, against 4,619 in all of 2024, and the UFLPA Entity List grew to 144 named entities. Adverse media screening of your direct supplier tells you nothing about a sub-supplier 3 tiers down in a language your database does not index.
UK enterprises carry Bribery Act 2010 “failure to prevent” exposure, where adequate procedures, including proportionate third-party due diligence, are the defense. The reputational duty is the same on both sides of the Atlantic: when a story about your counterparty breaks, the question is whether competent adverse media screening would have surfaced it earlier. “The database returned no match” is a weak answer if the story sat in the local press for months.
| Regime | What it now expects | Where a name-only screen fails |
|---|---|---|
| DOJ ECCP 2024 (US) | Data-driven vendor review across the whole relationship | Onboarding-only snapshot, no ongoing monitoring |
| UFLPA (US) | Importer proves no forced labor, multi-tier | No sub-tier or non-English visibility |
| Bribery Act 2010 (UK) | Proportionate, evidenced third-party diligence | No investigation trail to evidence adequacy |
| FATF Recommendation 10 (global) | Ongoing due diligence, current risk understanding | One-off match, no adjudication record |
If you are evaluating adverse media screening software or adverse media screening tools, the feature list most vendors lead with, source count, languages, match speed, tells you about the database. It does not tell you about the 4 blind spots that return clean. Score any tool against what it does beyond matching a name.
Score your adverse media screening program against 5 levels. Most enterprise teams outside financial services sit at Level 2. Regulators increasingly expect Level 4.
| Level | Name | What it looks like | Coverage and defensibility |
|---|---|---|---|
| 1 | Ad hoc | A name searched in a general web engine at onboarding. No defined sources, no record. | None. Indefensible on examination. |
| 2 | Database screen | A commercial adverse media screening tool run at onboarding. Hits cleared by a junior reviewer. | Top row of the map only. 4 blind spots open. |
| 3 | Screen plus review | Screening plus a documented human review of hits, with a rationale recorded. | Better evidence, same coverage gap. |
| 4 | Screen plus investigation | Screening plus open-source, multi-language investigation on risk-tiered cases, analyst-adjudicated. | Reaches the lower rows. Defensible. |
| 5 | Continuous intelligence | Level 4 plus continuous monitoring across the portfolio. | Closes the time-gap blind spot. |
A hit is raw. A decision is finished. Every adverse media screening program needs a clear line between the two. Adjudication is the analyst work that decides whether a match is your subject, whether the underlying event is material, and what to do about it. Regulators examine the decision, so the decision is what needs documenting.
| Test | Question it answers | Weight |
|---|---|---|
| Identity | Is the hit actually your subject, not a name twin? Resolve aliases and transliterations first. | Critical |
| Materiality | Does the underlying event change the risk? A 12-year-old dismissed claim is not a 2025 open investigation. | Critical |
| Source quality | How strong is the source? A court record and a forum post are not equal evidence. | Required |
| Recency and status | When did it happen and what is the current status? “Charged” and “acquitted” differ. | Required |
| Rationale | Is the decision and its reason written down? This is the artifact an examiner asks for. | Recommended |
Analyst-led investigation is the second layer on top of adverse media screening: trained investigators using open-source intelligence across languages, jurisdictions and the unindexed web, applying human judgment to what they find. It does not replace adverse media screening. It reaches the risk the screen cannot, and produces the decision the screen cannot.
3 capabilities matter. Native-language OSINT means a story in Portuguese or Mandarin is read by someone who reads it, not machine-translated from a headline that was never ingested. Entity resolution penetrates aliases and ownership layers before a decision gets made. Adjudication grades sources, dates events and records a rationale. Together, these move a program from a hit list to an enhanced due diligence file a board or a regulator can rely on.
In both cases, the database screen was necessary and insufficient. It cleared the name. The investigation found the risk.
A layered program does not investigate everything to the same depth. That would be slow and wasteful. It tiers the work by risk, so adverse media screening carries the volume and the analyst carries the judgment.
Start by running adverse media screening across the whole population against the database, as most teams already do. That covers the top row of the map: sanctioned entities, indicted individuals, structured English-language risk. Then route by risk tier. Low-risk, domestic, well-documented counterparties may need nothing more than a documented review of any hits. Medium and high-risk cases, cross-border suppliers, high-value targets, politically exposed counterparties, and anything in a UFLPA priority sector, go to investigation.
At the investigation stage, an analyst runs native-language open-source searches across the subject’s operating jurisdictions, resolves aliases and ownership layers, reads the underlying sources rather than the summaries, and grades what they find. The output is a decision: escalate, clear, or mitigate, with a dated and source-cited rationale attached. That rationale is the artifact that survives an examination.
Finally, put the higher-risk relationships under continuous monitoring so a new adverse event surfaces in days, not at the next annual review. This is what moves a program from a photograph to a film.
The instinctive fix is more data. It rarely helps, because the gap is not about volume. 4 of the 5 blind spots are categories of risk that never enter a structured database in the first place: unstructured reporting, alias and ownership concealment, vendor-filtered content, and events that postdate the last screen. A second database gives you more of the top row of the map and almost none of the bottom. The missing capability is investigation and judgment, not more rows.
A Neotas analyst reviews a sample of your current adverse media screening against open-source, multi-language investigation on the same subjects, and shows you exactly what a name-only screen missed.
Schedule a callThese mistakes show up again and again when Neotas analysts review a client’s existing adverse media screening process against an investigation on the same subjects.
The most expensive mistake. A no-match result gets recorded as a positive finding when it is the absence of a finding. Build the language into your policy: an adverse media screening result is not a risk conclusion.
If your third parties operate in non-English markets and your sources are English, your coverage gap equals your international exposure. This is fixable and rarely fixed.
Risk changes after you onboard. A program without any re-screen or monitoring is defending a decision made with information that expired the day after it was made.
With a 90 to 95 percent false-positive rate, teams pour hours into clearing noise. The effort feels like coverage. Tuning down noise does nothing for the risk your sources never held.
Recording a court judgment and an anonymous forum post as equal adverse media hits corrupts the risk picture in both directions. It inflates noise and buries signal.
If you cannot show why you cleared or escalated a hit, you screened but did not decide, and an examiner will treat it that way.
Answer each adverse media screening question honestly for your current process. Every “no” is a blind spot on your record.
| # | Question | Weight |
|---|---|---|
| 1 | Do you search sources in the native language of every jurisdiction your third parties operate in? | Critical |
| 2 | Can your process find a subject using an alias, maiden name or transliterated spelling? | Critical |
| 3 | Do you resolve beneficial ownership beyond the name on the contract? | Critical |
| 4 | For high-risk cases, do you read the underlying source, not just the database summary? | Required |
| 5 | Do you record a dated, source-cited rationale for every material hit? | Required |
| 6 | Do you grade source quality? | Required |
| 7 | Does anything re-screen or monitor a third party after onboarding? | Critical |
| 8 | If a story broke about a supplier tomorrow, would you surface it within days, not months? | Required |
| 9 | Can you produce the full investigation trail for any single decision? | Required |
| 10 | Do you know what percentage of your third parties operate in non-English markets? | Recommended |
| 11 | Have you defined which risk tier triggers investigation beyond a screen? | Recommended |
| 12 | Could you defend your process to an examiner using the DOJ ECCP 2024 questions? | Critical |
7 or more “no” answers put you at Level 2, where a single adverse event can become a problem you cannot defend. 3 to 6 put you at Level 3: good documentation, real coverage gap. 0 to 2 put you at Level 4 or above.
Different stakeholders in an adverse media screening program carry different primary exposure.
Your exposure is an adverse media screening result that clears names it should flag. You need a dated, source-cited adjudication record for every material decision, plus multi-language investigation on the cases that carry real risk. A cleared alert queue will not survive examination.
Your exposure sits in FATF Recommendation 10 and, in the EU, the Sixth Anti-Money Laundering Directive: ongoing due diligence, proportionate to risk, with evidence. A once-at-onboarding, English-only adverse media screening process leaves a gap that maps directly to your international footprint.
Your exposure is a sub-tier supplier 3 layers down, in a language your database does not index, in a UFLPA priority sector. Adverse media screening of your direct supplier tells you nothing about where forced-labor risk actually sits.
Your exposure is the DOJ ECCP 2024 standard and Bribery Act “adequate procedures.” Both are assessed on documented, risk-based investigation, not on whether a screen ran. The investigation trail is your defense.
How Neotas combines database screening with open-source intelligence and analyst investigation to produce regulator-ready EDD reports across 200+ languages, for the cases adverse media screening cannot resolve on its own.
The full TPRM guide covering how to build a program that satisfies DOJ, UK and EU expectations, including risk tiering, due diligence depth by tier, and governance.
How open-source investigation reaches the unindexed and local-language web that structured adverse media databases miss, with the methods analysts use to verify and document findings.
An overview of due diligence types, when each applies, and how screening, verification and investigation fit together across onboarding and monitoring.
Where adverse media screening, investigation and continuous monitoring sit across the vendor lifecycle, from onboarding through review to offboarding.
A practical checklist for scoping an EDD engagement, including the people, ownership and adverse media workstreams a name screen alone cannot satisfy.
How adverse media screening fits alongside sanctions and PEP screening in a financial crime compliance framework, and where analyst-led investigation adds defensibility.
Why forced-labor and sub-tier supplier risk, the exposure UFLPA puts on importers, needs multi-tier, multi-language investigation beyond a direct-supplier screen.
Adverse media screening, or negative news screening, is checking a person or company against collections of negative news and public records to flag links to financial crime, corruption, sanctions, terrorism financing or reputational harm. It is one input to a due diligence decision, not the decision itself.
A news report that a company director was charged with fraud, an NGO report linking a supplier to forced labor, a regulatory enforcement notice, a court judgment, or coverage of sanctions evasion. Adverse media can be structured, already tagged in a database, or unstructured, an article, blog or local-language report a database has not indexed.
Yes. Under FATF Recommendation 10, customer due diligence includes understanding the counterparty and conducting ongoing monitoring. Adverse media screening is a common way to satisfy part of that obligation, though regulators expect the depth of adverse media screening to match risk, with enhanced measures for higher-risk relationships.
Screening is a point-in-time check, usually at onboarding, that asks whether adverse media exists on a name today. Monitoring is continuous or scheduled and asks whether anything new has emerged since the last check. Screening makes the initial decision. Monitoring keeps it current for higher-risk relationships.
A clean screen means a name did not match a curated database. It does not mean no risk exists. Risk that is unstructured, non-English, hidden behind an alias or ownership layer, filtered out by the vendor, or that emerged after the screen ran will all return a clean result.
Adverse media is usually grouped into financial crime, sanctions and terrorism, regulatory and legal, ESG and conduct, and reputational and behavioral categories. It also splits by structure: structured adverse media is pre-tagged in a database, while unstructured adverse media, where much real risk sits, is not.
Screening matches a name against pre-collected data. Enhanced due diligence investigates a subject using open-source intelligence, entity resolution and analyst judgment, then produces a documented decision. Screening tells you whether there is a match. Enhanced due diligence tells you whether there is risk and what to do about it.
It adds the layer a database cannot provide: native-language search across jurisdictions, investigation of aliases and ownership chains, source-quality grading, and a dated, source-cited rationale for each decision. That reaches the risk a name screen misses and produces the evidence a regulator examines.
Screening once at onboarding leaves a time-gap blind spot, because risk changes after onboarding. Higher-risk relationships warrant continuous monitoring so new adverse events surface within days. The DOJ’s 2024 guidance is explicit that third-party management should continue throughout the relationship.
Most database tools weight toward English-language sources, and English is a minority of global news output. Reaching non-English risk requires native-language search and, for material findings, an analyst who reads the source language rather than relying on machine translation of a headline the system never ingested.
Look past source count and speed. Score the tool on native-language reach, coverage of unstructured sources, entity resolution across aliases and ownership, a documented adjudication trail, continuous monitoring, and whether an analyst layer exists for high-stakes cases. The key question is what the tool does about risk that is not in its sources.
For financial institutions, FATF Recommendation 10 and national AML regimes. For enterprises more broadly, the DOJ Evaluation of Corporate Compliance Programs (2024), the UK Bribery Act 2010, and forced-labor import law such as the US UFLPA all expect documented, risk-based third-party due diligence, not just a screen.
It can only match what someone already collected, tagged and included. Anything outside that curated set, whether unstructured, non-English, alias-hidden, filtered out, or newly emerged, returns a clean result. The weakness is not volume. It is the categories of risk that never enter a structured database.
The adverse media screening step can be automated, and should be for speed and coverage across a portfolio. The adjudication step, deciding whether a hit is your subject, whether it is material, and what to do, needs human judgment for high-stakes decisions. Automate the collection, investigate the material findings.
It records the subject identity confirmed, the event and its current status, the source and its quality, the materiality assessment, and the resulting decision with a reason. It is dated and source-cited, so months later an examiner or auditor can see not just that you screened, but what you decided and why.
Whether you are building from scratch, upgrading an existing program, or preparing for a DOJ or FCA examination, a 30-minute conversation will show you exactly where you stand and what to fix first.
Schedule a callThis article is provided for informational purposes and does not constitute legal or regulatory advice.
Download the report to understand where conventional adverse media screening stops, where hidden risk sits, and how analyst-led investigation closes the coverage gap.
Neotas Enhanced Due Diligence covers 600Bn+ Archived web pages, 1.8Bn+ court records, 198M+ Corporate records, Global Social Media platforms, and more than 40,000 Media sources from over 100 countries to help you screen & manage risks.
See what database screening misses and how analyst-led investigation closes the gap.
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|---|---|---|
| __cf_bm | 30 minutes | This cookie, set by Cloudflare, is used to support Cloudflare Bot Management. |
| AnalyticsSyncHistory | 1 month | Used to store information about the time a sync took place with the lms_analytics cookie |
| bcookie | 2 years | LinkedIn sets this cookie from LinkedIn share buttons and ad tags to recognize browser ID. |
| bscookie | 2 years | LinkedIn sets this cookie to store performed actions on the website. |
| lang | session | LinkedIn sets this cookie to remember a user's language setting. |
| lidc | 1 day | LinkedIn sets the lidc cookie to facilitate data center selection. |
| UserMatchHistory | 1 month | LinkedIn sets this cookie for LinkedIn Ads ID syncing. |
| Cookie | Duration | Description |
|---|---|---|
| li_gc | 2 years | Used to store consent of guests regarding the use of cookies for non-essential purposes |
| rl_anonymous_id | 1 year | Generates an unique anonymous Id to identify a user and attach to a subsequent event. |
| rl_user_id | 1 year | to store a unique user ID for the purpose of Marketing/Tracking |
| Cookie | Duration | Description |
|---|---|---|
| _ga | 2 years | The _ga cookie, installed by Google Analytics, calculates visitor, session and campaign data and also keeps track of site usage for the site's analytics report. The cookie stores information anonymously and assigns a randomly generated number to recognize unique visitors. |
| _gat_gtag_UA_107495977_1 | 1 minute | Set by Google to distinguish users. |
| _gat_UA-107495977-1 | 1 minute | A variation of the _gat cookie set by Google Analytics and Google Tag Manager to allow website owners to track visitor behaviour and measure site performance. The pattern element in the name contains the unique identity number of the account or website it relates to. |
| _gcl_au | 3 months | Provided by Google Tag Manager to experiment advertisement efficiency of websites using their services. |
| _gid | 1 day | Installed by Google Analytics, _gid cookie stores information on how visitors use a website, while also creating an analytics report of the website's performance. Some of the data that are collected include the number of visitors, their source, and the pages they visit anonymously. |
| attribution_user_id | 1 year | This cookie is set by Typeform for usage statistics and is used in context with the website's pop-up questionnaires and messengering. |
| CONSENT | 2 years | YouTube sets this cookie via embedded youtube-videos and registers anonymous statistical data. |
| Cookie | Duration | Description |
|---|---|---|
| _fbp | 3 months | This cookie is set by Facebook to display advertisements when either on Facebook or on a digital platform powered by Facebook advertising, after visiting the website. |
| fr | 3 months | Facebook sets this cookie to show relevant advertisements to users by tracking user behaviour across the web, on sites that have Facebook pixel or Facebook social plugin. |
| IDE | 1 year 24 days | Google DoubleClick IDE cookies are used to store information about how the user uses the website to present them with relevant ads and according to the user profile. |
| test_cookie | 15 minutes | The test_cookie is set by doubleclick.net and is used to determine if the user's browser supports cookies. |
| VISITOR_INFO1_LIVE | 5 months 27 days | A cookie set by YouTube to measure bandwidth that determines whether the user gets the new or old player interface. |
| YSC | session | YSC cookie is set by Youtube and is used to track the views of embedded videos on Youtube pages. |
| yt-remote-connected-devices | never | YouTube sets this cookie to store the video preferences of the user using embedded YouTube video. |
| yt-remote-device-id | never | YouTube sets this cookie to store the video preferences of the user using embedded YouTube video. |
| yt.innertube::nextId | never | This cookie, set by YouTube, registers a unique ID to store data on what videos from YouTube the user has seen. |
| yt.innertube::requests | never | This cookie, set by YouTube, registers a unique ID to store data on what videos from YouTube the user has seen. |