Data breach impact simulation with BigID

Run a data breach simulation to see what, and who, it could reach, then lock it down before it happens

With BigID, you can run a data breach simulation against your real environment. Choose who is compromised and how they attack, and BigID shows the sensitive data, the people, and the AI agents within reach, the regulations a data breach would trigger, and the fewest changes that close the paths. It is powered by BigID AgentIQ, working from what BigID has already discovered.

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Who and what

Every identity, mapped to its reach

Employees, service accounts, external users, and AI agents, each simulated for the sensitive data they could reach

Your environment

Run on what BigID already found

Your sensitive data, your access, your AI assets, and your open risk cases, in place of a generic model of a data breach

Your next move

Close the paths, then confirm it

A plan of the fewest changes, assigned to people or agents, and re-simulated to show the exposure drop

Why security teams simulate data breaches in BigID

A data breach simulation worth trusting starts from a map of your own environment

Data breach planning gets sharper when it runs on facts about your own environment. BigID has already discovered your sensitive data, the identities and permissions that reach it, and the places AI touches it, so every simulation answers "what could this data breach reach?" with your own records, your own identities, and your own obligations.

Start from your real environment

Sensitive data, access, AI assets, and open risk cases, already mapped. BigID has classified your data, resolved who can reach it through direct permissions, inherited folders, groups, and sharing links, and found the AI assets that touch it. The simulation starts from that map, so nothing is estimated and nothing is re-entered.

Choose the data breach

Pick who is compromised and how they attack. Run exfiltration, ransomware, or destruction for any employee, service account, external user, or AI agent. BigID follows only the access that attack can use, and shows the records, regulations, and outside exposure in reach.

Lock down what it finds

Turn the simulation into a plan that removes the exposure. BigID ranks the fewest changes that cut the most exposure, assigns each one to a person or an agent, and runs the simulation again to confirm the drop. Changes to your data run only after a person approves them.

See it on a real environment

Watch a data breach simulated end to end

Data Breach Impact Simulator: See Your Blast Radius Before It Happens | BigID

BigID AgentIQ reads BigID's map of your sensitive data, access, and AI exposure, simulates the most likely data breaches, sizes the blast radius, and hands a prevention plan to your people and agents.

2:41 / AgentIQ

What you will see

The map BigID already hasSensitive data, access, AI exposure, and open risk cases AgentIQ at workAI reasoning over BigID findings, within the user's permissions Choosing the data breachExfiltration, ransomware, or destruction, and the paths each can use Who to run firstIdentities ranked by likelihood of compromise and records at risk The blast radiusRecords, files, escalation paths, and the obligations they trigger The prevention planThe fewest changes, assigned to people and agents, then re-simulated Prefer to click through it yourself? Take the interactive tour

Data breach impact simulation is one example of AI-native work enabled by Agente BigID IQ, the Agentic Automation Layer for Data & AI Security and Compliance. AgentIQ combines AI reasoning with BigID's findings, working through the BigID MCP with the user's own permissions, so you can ask, understand, decide, act, and automate.

AgentIQ reasons over what BigID knows, then walks every attack path for you

An AI agent queries BigID through the BigID MCP server with the user's own permissions, and works through the simulation step by step. The same run works on any data source that carries access control lists.

Findings come straight from BigID, so the simulation covers your catalog, your access intelligence, your group memberships, and your open DSPM cases without anyone exporting a spreadsheet.

AgentIQ stays inside the role of the person running it. It calls BigID APIs with that person's permissions, logs every action to a named person, and sends changes to a person for approval.

Every identity gets its own attack paths, scored by the sensitivity of the data it reaches and the regulations that data triggers.

Pick who is compromised and how they attack

BigID keeps only the access that attack can use, so each result reflects the way that kind of data breach works.

Exfiltration

Entry: A stolen password, an OAuth token, or a service account key
Counts: Any path with read access
Impacto: Records copied out, and notification clocks start, including GDPR's 72 hours

Ransomware

Entry: A compromised identity that can write
Counts: Only paths with write or delete access
Impacto: Data is encrypted, and read-only access stays safe

Destruction

Entry: Wiper malware or a malicious insider
Counts: Only paths with delete access
Impacto: Data is destroyed, and recovery depends on backups

From there, the data breach can move through

Permissões diretas Permissões de pasta herdadas Adesão ao grupo Sharing links and external users Credentials found in your data

AI agents and copilots are simulated the same way, because an agent can reach everything the identity it runs as can reach. Credentials that BigID finds sitting in scanned data become escalation paths, so a read-only identity that can see a stored key is simulated as the identity that key opens.

Comece aqui

Start with the question your team is asking

What could a data breach reach in our environment? Choose an identity and an attack, and BigID shows the sensitive records, the files, and the data already reachable outside the company, with the regulations each would trigger. Size the blast radius → Which people and accounts are most likely to be compromised? BigID ranks every identity by likelihood of compromise and records at risk, so you start with the service account, external user, or offboarded employee that matters most. Rank the identities → How could one compromised identity lead to another? BigID follows direct permissions, inherited folders, groups, sharing links, and credentials found in data, and flags the combinations that let one identity take over another. Follow the paths → What could our AI agents and copilots reach? Agents reach data through the identities they run as, so BigID simulates each agent through its identity and shows the sensitive data in reach. Manage AI access risk → What is the fewest changes that would cut the exposure? BigID prescribes tasks ranked by the records each one protects, and shows the exposure removed as each task lands. Build the plan → How do we lock it down, and show that it worked? Hand the plan to AgentIQ: agents act after approval, tickets go to people, and the simulation re-runs to confirm the exposure dropped. Lock it down →

Capacidades

Every identity, every attack, every fix

Sizing the blast radius, ranking who is most at risk, following the attack, managing AI access risk, and locking down the paths, all on the data and access BigID already holds.

Size the blast radius

See what a data breach would reach in your own records, files, and obligations.

Sensitive records the compromised identity could reach, counted across every data set it can touch
High-sensitivity files called out, along with data that is already reachable outside the company
Records exposed by category, including health and medical, personal information, national ID, financial data, and secrets and keys
Notification and response obligations that the exposed data triggers, including GDPR's 72-hour window, US state breach notification laws, CCPA/CPRA, HIPAA, PCI DSS, and the GLBA and FTC Safeguards Rule
A flow from compromised identity to access path, data reached, classification, and obligations
Estimated data breach cost, built from a cost-per-record figure you set
A worst case view that picks the identity with the greatest reach

Know who to worry about first

Likelihood of compromise, weighed against the records at risk.

Identities ranked by likelihood of compromise and the records each could affect
Likelihood drawn from identity type, external and offboarded status, group membership, open tasks, and credentials found in scanned data
Service accounts with long-lived keys and no MFA flagged, along with the sensitive data they can reach
Offboarded employees who still hold access, and external identities, surfaced with the data in their reach
Employees, service accounts, external users, and AI agents simulated on the same footing
Identities that share the same reach grouped together, so one fix covers them all

Follow the attack through your environment

Choose the data breach, and BigID keeps only the access that attack can use.

Exfiltration counts read paths, ransomware counts write and delete paths, and destruction counts delete paths
Direct permissions, inherited folder permissions, and group membership followed laterally
Sharing links and external users included as paths into the data
Credentials found in data become escalation paths, so a stored key that opens another account is part of the simulation
Risky combinations surfaced, such as a non-human identity with destructive rights on high-impact data
Works across any data source that carries access control lists

Manage AI access risk

Agents and copilots reach data through the identities they run as, and BigID simulates them that way.

AI agents and copilots simulated through the identity each one runs as
AI assets discovered, including vector stores, models, and datasets, with the sensitive data each holds
AI vectors that hold confidential data flagged, along with models that use restricted data
Agent reach scoped down by reducing the permissions behind its identity
Re-simulation after each change shows an agent's reach shrinking
Pairs with AI governance and AI assistant readiness for ongoing control of what agents can reach

Reduce the blast radius with the fewest changes

A prevention plan that says what to do first, and what each change removes.

Prevention plan ranked by records protected, with cumulative exposure removed as each task lands
A fewest-changes view that can contain a data breach with a single task, such as rotating credentials stored in data
Redundant paths removed, such as direct permissions that duplicate a group membership
Offboarded accounts disabled and external access revoked as tasks in the plan
Priority and due date on every task, with an owner chosen from people, teams, or an AI agent
Plan copied as a summary or CSV, or sent straight to an agent

Lock it down and confirm it

Hand the plan to people and agents, then run the simulation again.

Send to AgentIQ turns the plan into instructions that agents carry out after a person approves
Tickets open for the people who own the remaining tasks, with an owner and a due date
Remediation actions in BigID, such as revoking external access and open access, run by an agent after approval
Every action logged to a named person, with BigID permissions applying to the agent as they do to the user
Re-simulation shows the exposure drop, so the result of each change is on the record
Works from BigID or from an agentic platform such as Claude, ChatGPT, or Copilot

An example run

From the identity most likely to be compromised to a plan that removes 98% of the exposure

One simulation in the BigID demo environment, from the first ranking to the prevention plan.

Who to run first

Ransomware, ranked by likelihood of compromise and records at risk

1Service account with long-lived keys and no MFAMédioCould encrypt 11,042 records. Its keys sit in data that other accounts can read, which opens a path to a second service account.
2Project service account with long-lived keysMédioCould encrypt 76 records.
3External userMédioCould encrypt 14 records.
4External userMédioCould encrypt 14 records.
5Offboarded user who still has accessAltoCould encrypt 6 records, with access both directly and through a group.

What the data breach reaches

The first service account, compromised by ransomware

11,042sensitive records an attacker could encrypt, across 13 data sets

41high-sensitivity files

5data sets already reachable outside the company

7notification obligations triggered, including GDPR's 72-hour window

What closing it takes

The prevention plan across every identity in scope

Record exposures found88,598

After 14 tasks1,918

98%of the exposure removed by the plan, with the first task alone protecting 11,042 records

The first task rotates the credentials stored in a data bucket, which takes that service account's exposure to zero.
Owners come from your teams and your agents: cloud platform, IAM, data owners, and AI agents.

Results from a simulation of exfiltration and ransomware across 13 identities (22 scenarios) in the BigID demo environment.

AI access risk

Simulate AI agents like any other identity, and scope their reach down

An AI agent never touches data directly. It reaches data through the identity it runs as, which means an agent can reach everything that identity can reach. BigID simulates agents and copilots the same way it simulates employees and service accounts, so you can see what a compromised or misdirected agent would expose, and reduce that reach ahead of time.

Agents are simulated through their identities. BigID maps the permissions behind each agent and copilot, then runs the same attack types against them.

BigID knows where AI touches sensitive data. In the BigID demo environment, that map includes 261 AI vectors holding confidential data among 343 AI assets, plus 5 models with restricted data.

Scope the reach, then confirm it. Reduce what an agent's identity can reach, run the simulation again, and see the exposure shrink. prontidão do assistente de IA e Governança de IA keep it that way.

Cenários

How security teams put data breach simulation to work

Size a data breach before the board asks

A CISO needs a grounded answer to "what would a data breach in our cloud storage reach?" before the next review.

BigID simulates the compromise of the most likely identity, shows the records, files, and obligations in reach, and gives an answer built on your own data.

Size the blast radius →Know who to worry about first →

Find the one credential that opens everything

A service account can read a bucket that holds another account's keys.

BigID follows the credentials found in your data, flags the escalation to the second account, and recommends rotating the stored keys as the first task.

Follow the attack →Fewest changes →

Check AI access before the assistant rollout

Copilots and agents are about to reach data across the company, through identities that already hold broad access.

BigID simulates each agent through the identity it runs as, shows the sensitive data in reach, scopes that reach down before rollout, and re-runs the simulation to confirm.

Manage AI access risk →Fewest changes →

Hand the fix to the people and agents who can make it

The plan touches cloud storage, identity, and data owners, each on a different team.

BigID gives every task an owner and a due date, sends agent tasks to AgentIQ after approval, opens tickets for people, and re-simulates to show the exposure went down.

Lock it down →Build the plan →

Verificação

A recognized platform, with the controls a security review checks

Recognized from outside

Líder no relatório The Forrester Wave™: Soluções de Descoberta e Classificação de Dados Sensíveis, 2º trimestre de 2026, com a pontuação máxima de 5 em 5 em onze critérios.
Vencedor do desafio de classificação de dados da Intuit entre 20 fornecedores, com precisão e recall ponderados de 96,5%.

Veja a avaliação da Forrester →

O que uma revisão de segurança verifica?

Control and accountability

✓AgentIQ works within the running user's BigID permissions
✓Every action logged to a named person
✓Human approval before any change
✓Plans exported as a summary or CSV

Results you can explain

✓Likelihood signals shown for every ranked identity
✓Attack type and path shown with each result
✓Obligations tied to the data categories that trigger them
✓Simulation run again after each change

Perguntas frequentes

Answers on data breach types, AgentIQ, AI agents, and the plan that follows

What is a data breach impact simulation?

A data breach impact simulation shows what a data breach would reach before it happens. You choose who is compromised and how they attack, and BigID follows the access that attack can use to show the sensitive records, files, AI assets, and obligations in reach, then recommends the fewest changes that reduce them.

Does it run on our environment?

It runs on your environment. Every simulation starts from what BigID has discovered in your connected data sources: sensitive data findings, permissions and group memberships, AI assets, and open DSPM cases. It works across any source that carries access control lists.

What does AgentIQ do in the simulation?

AgentIQ is the agentic layer that makes the simulation possible. An AI agent queries BigID through the BigID MCP server, using the user's own permissions, and works through the simulation step by step: walking attack paths for each identity, scoring impact by sensitivity and regulation, and finding the fewest changes that cut the most exposure.

Which types of data breach can we simulate?

Exfiltration, ransomware, and destruction. Exfiltration counts any path with read access, ransomware counts write and delete paths, and destruction counts delete paths. The simulation moves through direct permissions, inherited folders, group membership, sharing links, external users, and credentials found in data.

Can we see which people and accounts are most likely to be compromised?

Sim. BigID ranks employees, service accounts, external users, offboarded accounts, and AI agents by likelihood of compromise and records at risk, so you can see which identities matter most before you choose a scenario.

How does it handle AI agents and copilots?

BigID simulates an agent by simulating the identity it runs as. Agents reach data through identities, so you see the sensitive data an agent could reach, alongside the AI assets BigID has discovered. You can scope the reach down and run the simulation again to confirm.

Which regulations does it factor in?

BigID ties exposed data to the obligations it triggers. Results show GDPR's 72-hour notification window, US state breach notification laws, CCPA/CPRA, the HIPAA Breach Notification Rule, PCI DSS, the GLBA and FTC Safeguards Rule, and credential compromise response, each linked to the data categories that trigger it.

What happens after the simulation?

BigID produces a prevention plan. Tasks are ranked by the records each one protects, with an owner and a due date. Agent tasks run through AgentIQ after a person approves them, tasks for people become tickets, and the simulation re-runs to show the exposure dropped.

Does BigID change anything without approval?

Não. Changes need human approval, every action is logged to a person, and AgentIQ works within the permissions of the user who runs it.

Leve isso adiante

Where to go deeper on data breach impact simulation

Video / start here

Data Breach Impact Simulator: See Your Blast Radius Before It Happens

Watch AgentIQ read BigID's map, rank the identities most likely to be compromised in a data breach, size the blast radius, and hand a prevention plan to people and agents.

Watch on YouTube →

Agente IQ Agente BigID IQ The Agentic Automation Layer for Data and AI Security and Compliance, which delivers BigID wherever you work so you can ask, understand, decide, act, and automate. Explore AgentIQ → Governança de acesso Data access governance for employees and AI agents Permission reviews, activity monitoring, and automated revocation on the same access data that feeds every data breach simulation. Consulte a governança de acesso → prontidão do assistente de IA AI assistant readiness for Copilot, Gemini, ChatGPT, and Claude See what an assistant would surface before rollout, fix oversharing, and keep assistants scoped after go-live. See assistant readiness →

Bring the data breach your team worries about most.

Pick the identity, the attack, or the AI agent your team is most concerned about, and see the simulation run, from the blast radius to the prevention plan.

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