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AI agent security

AI agent security: closed by default, at every layer.

An agent reads untrusted text and holds real permissions. Every layer between the two is a control.

Botminds agents start with nothing: no tools until they are granted, no data beyond the collections they are given and the rows each person may read, no change without the approvals you choose. Hostile files and code run inside walls, every release is signed and verified, and every change lands on an audit trail you can prove.

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What is AI agent security?

AI agent security is protecting systems in which language models read data and act through tools. Because a model cannot reliably tell instructions from data, any document, email or page an agent reads can try to steer it. Strong AI agent security limits what a steered agent can reach: tools granted one by one, data filtered per person, changes approved by people, hostile content contained and every step on record.

Scales out on KubernetesServices and workers add capacity as work arrives, and give it back when it is done.
Built for millionsMillions of documents, records and agent runs — the indexes and workers grow with them.
Any cloud, any regionAzure, AWS, Google Cloud or on-premises, in the region your data must stay in.
Always onRedundant services, rolling releases and continuous backups, for mission-critical work.
Secure and governedSingle sign-on, encryption, least privilege and a sealed audit trail.
AI agent security risks

AI agent security risks, as a threat model.

A chatbot steered wrongly gives a bad answer. An agent steered wrongly sends the email, moves the money or deletes the record. STRIDE, the classic threat model, fits an agent well — here is each threat and the controls that answer it.

Zero trust for AI agents

Defense in depth: nine layers, each holding on its own.

No single control stops every attack on an agent. So every call passes nine layers, and each holds even when the one outside it is beaten — zero trust, applied to agents.

Identity and access

Securing AI agents starts with who they work for.

An agent acts for a person. That person passes four gates before the agent touches anything, so one agent gives each person exactly the answers they may see.

A workspace’s access policy
membership       SSO, your domains
two-factor       required
who may invite   workspace admins
credentials      issued by the workspace
join role        client, never admin
lifecycle        workspace admins suspend
  • SSO-locked workspacesMembership follows your identity provider and your domains.
  • Credential ceilingsThe platform sets how far a workspace may issue credentials; a workspace policy stays inside it.
  • Suspension that bitesA suspended person is out on their next request.
  • Review and previewSee who can reach what at platform, workspace and project scope, and view a page as a role sees it.
Least privilege for AI agents

Least privilege for AI agents: granted by name, failing closed.

The riskiest default in agent software is “every tool unless told otherwise”. Botminds runs the other way: an agent reaches what it was granted, and a missing setting falls back to read-only basics.

An approval, as the reviewer sees it
Agent

I matched 14 supplier invoices to purchase orders. Releasing them moves them to Approved and notifies accounts payable. Approve, reject, or edit the list?

You

Edit: hold the two from the new supplier until we verify their bank details.

Agent

Done — 12 released, 2 held. Your decision and the edit are kept with this conversation’s record.

Prompt injection protection

Prompt injection protection: documents are data, not instructions.

Direct injection comes from the person typing; indirect injection hides in what the agent reads — an invoice, an email, a web page. Both meet the same answer: what an agent may do is fixed in settings before it reads a word, and nothing it reads can change them.

written rules

Guardrails steer

Short rules beside the job — “never quote a full account number”. Reusable across agents, they shape what the model says.

enforced

Grants limit

What an agent can reach at all. A tool that was never granted is refused, however the model is persuaded.

a person decides

Approvals stop

On the tools you choose, the run pauses in the conversation until a person approves, rejects or edits it.

AI content safety

Content safety: the provider’s filters, surfaced and handled.

Model providers score each request for harm and for attacks. Botminds reads those verdicts — which category fired, never the words — and handles each refusal the way a careful operator would.

Data protection

Data protection from the browser to the model and back.

Data is protected at every stop: on the wire, in storage, inside a prompt, at the model and when a document leaves. Switch on PII masking for a deployment and emails, phone numbers, card and ID numbers are masked before the model sees them.

Multi-tenant isolation

Tenant isolation that fails closed.

Tenancy is a required argument, never a convention. Each workspace holds its own people, policies, agents, keys and audit chains — and the rules between workspaces drop anything that cannot prove where it belongs.

Containment

Containment for the files and code around agents.

Agents open office files, unpack archives, render cards builders wrote and run workflow steps. Each is a way in, so each is held: cards and conversions run inside walls, archives are checked before they open, and code is read before it runs.

AI supply chain security

Supply chain: signed images, checked packages, no secrets inside.

An agent platform is software plus the solutions installed on it. Both arrive through a chain — build, sign, release, package, install — and every link is checked.

Disconnected installs, too. Disconnected sites take no inbound access and no VPN to us. Each release arrives as a checksummed bundle of images whose signatures all verify, and carries no secrets. See private AI deployment.

Audit and forensics

An audit trail you can prove, and runs you can replay.

After an incident the first question is “what happened?”. Changes land on a trail sealed entry by entry, and every agent run keeps its own record — the request, each tool call, each approval and the answer — ready to replay.

Detection and response

Detect, contain, recover — and measure the next change.

Security operations for agents is a loop. The platform watches itself, tells the right people, freezes what must hold still, restores what changed, and scores the next change before it reaches anyone.

Governance of change

  1. Snapshot

    A labelled restore point before a risky change, with background snapshots in between.

  2. Diff

    See exactly which settings a change touched, module by module.

  3. Evaluate

    Score the changed agent against golden datasets; a worse score is flagged and recorded beside the change.

  4. Approve

    A package reaches production when an approver accepts its promotion.

  5. Roll back

    Read the diff, then return any module to a snapshot.

OWASP LLM Top 10

The OWASP Top 10 for LLM applications, control by control.

The OWASP Top 10 for LLM Applications 2025 is the most used list of what goes wrong in systems built on language models. Bring it to any agent platform. Here is how Botminds answers each item.

AI agent security framework

An AI agent security framework: MITRE ATLAS and the NIST AI RMF.

MITRE ATLAS sets out how attacks on AI systems unfold. The NIST AI Risk Management Framework sets out how an organisation governs AI risk. Both read straight onto the platform’s controls.

NIST AI RMF: govern, map, measure, manage

Mappings to test. OWASP, MITRE and NIST publish guidance. These mappings are our reading of where each control applies — bring them to your security review and test each one on a running deployment.

Compliance

Security and privacy: SOC 2 Type II, ISO 27001, GDPR.

The controls above sit inside a security and privacy programme: SOC 2 Type II audited controls, information security managed to ISO 27001, and privacy by design for GDPR. The detail, the reports and the badges are on security at Botminds.

audited

SOC 2 Type II

Independently audited controls over how customer data is handled, continuously.

standard

ISO 27001

Information security managed to the international standard.

privacy

GDPR

Privacy by design and by default, with data residency controls for regional requirements.

recurring

Penetration tested

Recurring security audits and penetration tests of the platform.

AES-256 · TLS

Encrypted everywhere

AES-256 at rest and TLS on the wire — all data, all the time.

by design

No training on your data

Your inputs and outputs never train models.

Enterprise scale

Security that holds at enterprise scale.

Thousands of people, thousands of agents and millions of documents and runs meet the same checks at the same door — in every replica, every region and every cloud. Security scales out with the platform, never in front of it.

Side by side

Chatbot security vs agent security vs Botminds.

Securing a chat assistant is mostly about what it says. Securing an agent is about what it can do. Teams that build agents themselves tend to discover the second list one incident at a time.

Chat assistantAgent you build yourselfBotminds
Planted instructionsA bad answerWhatever the agent can reachWhat it was granted; approvals where you set them
Tool accessNoneWhatever the code passes inGranted by name; fails closed to read-only
Data accessThe index it was givenOften a service account’sEach person’s read rules, on every search
TenancyThe vendor’sYour conventionA tenant on every block of context; fails closed
Content safetyThe provider’s filterAn error messageCategories recorded, refusals handled, care on self-harm
Files and codeRarely handledYour own sandboxingIsolated conversion, sandboxed cards, checked archives and code
Supply chainThe vendor’sYour pipelineSigned images, signature-checked packages, secrets stripped
Record and responseChat historyLogs, if you add themSealed audit chain, run replay, detectors, freeze, rollback
Questions people ask

AI agent security, answered.

More from the platform: the full architecture, and how security works at Botminds.

Security at BotmindsAI governanceHuman in the loopPrivate AI deployment
What is AI agent security?

AI agent security is protecting systems in which language models read data and take actions through tools. Because a model cannot reliably separate instructions from data, AI agent security limits what a steered agent can do: least-privilege tools, human approvals, per-person data rules, contained files and code, and a provable record.

What are the biggest AI agent security risks?

Prompt injection, excessive agency, data leakage to the wrong person, unreviewed actions, hostile files, supply-chain tampering, runaway spend and a missing record. A STRIDE threat model groups them as spoofing, tampering, repudiation, disclosure, denial of service and elevation of privilege — the threat model above maps a control to each.

How do you protect AI agents from prompt injection?

Treat everything an agent reads as data, and fix its tools and reach in settings it cannot change. A planted instruction can then steer the agent within what it was already allowed, tools set to need approval still wait for a person, and the provider’s filters flag jailbreaks and document attacks.

What is indirect prompt injection?

Instructions hidden in content the agent reads — white text in an invoice, a line in an email, a comment on a web page — rather than typed by the user. Botminds answers it the same way as direct injection: the content is data, and the agent’s grants, read rules and approvals hold whatever it says.

What is least privilege for AI agents?

Giving each agent the tools and data its job needs, and nothing else. In Botminds tools are granted by name; an agent with none gets read-only basics, and one whose settings cannot be read falls back to the same basics — never the full catalogue.

What does zero trust mean for AI agents?

Verify every call, grant the least and assume a breach. In Botminds every call is checked for identity, tenant, role and read rules at every door, agents reach only what was granted, and nine layers each hold on their own.

Is there an AI agent security framework?

Several. The OWASP Top 10 for LLM Applications lists the risks, MITRE ATLAS lists attacker tactics, the NIST AI RMF sets out govern, map, measure and manage, and STRIDE structures a threat model. This page maps Botminds controls to all four.

What are AI guardrails?

In Botminds, guardrails are written rules given to an agent, such as “never quote a full account number”. They steer the model; tool grants, read rules and approvals enforce what an agent may reach and change.

Can an AI agent take actions without approval?

With the tools it was granted, and you choose which of those wait for a person. A person then approves, rejects or edits the request in the conversation, and the decision is kept with the conversation’s record.

How does Botminds keep agents from leaking data?

An agent reads the collections it is attached to, and when a person asks, every search is filtered by that person’s read rules — the assistant’s own search included. When an agent answers by email, in Teams or over the API, it searches what it was given, so attach to a widely reachable agent the collections everyone who reaches it may read.

How does AI agent security scale?

The checks are stateless code in every replica, so capacity grows by adding replicas on Kubernetes. Audit chains are kept per project and written side by side, and the same signed release runs on Azure, AWS, Google Cloud or on-premises, in any region.

Is Botminds certified?

Botminds’ controls are independently audited for SOC 2 Type II, information security is managed to the ISO 27001 international standard, and privacy is built in by design for GDPR, alongside recurring penetration tests. The detail is on security at Botminds.

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