Table of contents
Highlights
- The biggest security shift with agentic platforms is actuation risk: the agent can change systems of record, not just answer questions. That changes your threat model from data exposure to data and access misuse.
- Prompt injection is rarely a single-step problem in agentic workflows. It can propagate through tools, memory, and multi-step plans. Evaluations should focus on containment and authorization boundaries, not only input filtering.
- A security claim is not a control until you can validate evidence such as scoped permissions, tenant isolation design, and action-level audit logs. Procurement can move faster when you ask for proof artifacts early.
- Policy gates and change control determine whether agents stay within least-privilege boundaries during real operations. Approvals and change control matter as much as the architecture itself.
- Shadow AI pressure often rises when sanctioned tools are limited to read-only experiences. A platform that enables secure, approved actions may reduce the incentive for unapproved agent tools.
- Moveworks is built with security and privacy by design, applying identity validation through deterministic systems, prompt injection protection, and data minimization so enterprises may be better positioned to deploy agentic AI in regulated environments, with controls designed to support compliance and governance requirements.
A 2024 IBM Institute for Business Value study found that only 24% of GenAI projects had a security component, even though 82% of respondents called secure and trustworthy AI essential to their business — a gap that IBM's more recent breach research suggests hasn't closed: its 2026 Cost of a Data Breach Report still found 68% of breached organizations lacked any AI governance policy. The gap between intent and action is wide, and it gets wider as AI moves from answering questions to taking actions.
For many organizations, the current wave of AI investment began with generative AI. Autonomous agents are a different commitment.
When your security team asks for proof that an agent stayed within its boundaries during an access provisioning workflow, you need more than a demo. You need logs, scoped permissions, and a clear record of what the agent did and why. That gap between what agents can do and what teams can verify is where most agentic deployments stall in the first 90 days.
Autonomous agents that can create a ServiceNow ticket, update Workday fields, or trigger an identity workflow carry meaningfully different risk than a read-only assistant. The risk isn't just data exposure. It's access misuse, unauthorized action, and the accountability gap that follows when a workflow runs without a traceable record.
Secure architecture choices may help your team deploy agents with clearer boundaries, better logs, and fewer workarounds that introduce new risks.
What makes an agentic AI platform secure?
A secure agentic AI platform is designed to act across enterprise systems within defined boundaries, with controls in place to help minimize the risk of unauthorized access or scope creep. That means least-privilege access, strong isolation, authorization enforcement at every action, and auditable accountability for every step the agent takes.
Agents should only have access to the data a specific task requires. Strong agentic platforms typically support the three dimensions security teams look for across enterprise tools: confidentiality, integrity, and availability.
For agentic systems, those extend further. Confidentiality covers data minimization and scoped access. Integrity includes action-level audit trails and change control. Availability maps to reliable execution and rollback paths when a multi-step workflow partially fails.
Governance is the policy and approval layer that sits above those technical controls, determining what's acceptable in terms of usage and under what conditions an agent can act. Clear guardrails can give employees more confidence to use AI, which tends to support broader, safer adoption.
The division of responsibility matters here. Security teams own risk controls, audit readiness, and incident response planning. IT handles reliability, change management, and keeping core systems running. Both roles have a stake in agentic AI, and keeping those lanes clear tends to make evaluation and deployment faster.
Agentic threat model: What can go wrong
A chatbot answers questions, while an agent takes action on your behalf. That difference changes the security picture significantly.
For security and IT buyers, the right controls can help reduce the risk of larger operational problems, especially as agent workflows grow in scope and complexity.
Injection, exfiltration, escalation, and context poisoning
Agentic AI doesn't change the fundamentals for security teams, but it does add a new layer to evaluate. The threat categories below map to OWASP AST10-style risk categories, which organize agentic security risks around injection, data exposure, excessive autonomy, and insecure tool use.
Risk Category | What It Means | Control |
Prompt injection | A malicious input changes how an agent completes a task | Input validation, tool restrictions, and approval workflows |
Data exfiltration | Sensitive information is exposed through connected systems | Least-privilege access, content controls, and audit logs |
Privilege escalation | An agent gains broader permissions than necessary to perform a task | Role-based access controls and authorization checks |
Context poisoning | Incorrect or manipulated information influences future actions or workflows | Trusted data sources and context validation |
With multi-step workflows, a small mistake can ripple through every subsequent action. That's why authorization boundaries and action containment tend to matter as much as input filtering.
A few scenarios show how this plays out in practice:
- An agent processes a ticket comment containing a hidden instruction to change the destination email on an outbound notification. That's prompt injection altering tool arguments in real time.
- An agent with access to a help desk tool surfaces sensitive HR data in a public ticket field because its output wasn't scoped correctly. That's exfiltration through a connected system.
- An agent handling a password reset requests broader identity permissions than the task requires, and no policy gate fires.
Don’t make the mistake of thinking of these as edge cases. They're the authorization and containment gaps that enterprise evaluation needs to surface.
Supply chain risk in tools and integrations
Agents rarely work alone. They rely on plugins, connectors, and toolchains to function, and each integration carries its own permission scope and failure modes. A single badly-behaved plugin can expose or misroute data across an entire agent workflow, so the supply chain question is less about whether integrations exist and more about how they're governed.
During vendor evaluation, ask how plugins are reviewed before they're made available and whether there's a change log for plugin updates. When reviewing connectors, ask vendors to show the permission scope each integration requests, not just whether a review process exists. A plugin with broad read permissions across your identity system is a different risk than one scoped to a single endpoint.
Ask how the platform handles failure from third-party tools too. Does it validate tool responses before acting on them? Can it quarantine or disable a tool that starts returning unexpected data? What does the rollback path look like if a plugin behaves correctly in testing but fails in production?
These questions tend to separate vendors with mature integration governance from those with a connector list and little else.
Security controls and governance requirements to evaluate
Where threat categories help you understand what could go wrong, buyer requirements tell you what to verify. Here's what many security teams are asking for during vendor evaluation.
Identity, authorization, isolation, and encryption
Identity and authorization are two distinct controls worth separating in any evaluation. Identity establishes who or what is requesting access. Authorization determines what that identity is allowed to do.
An agent can have a verified identity and still be tightly restricted by its assigned permissions. In practice, that means agents should operate on scoped tokens tied to a specific task or workflow, rather than broad admin credentials. An agent that needs to update a single ITSM field doesn't need the same permissions as a platform administrator.
This matters more in multi-tenant environments. In a well-designed multi-tenant architecture, each customer's agent operates in isolation, with no path to another customer's data or systems, even on shared infrastructure.
Encryption is another area to evaluate when assessing any agentic AI platform. Expect data to be protected both in transit and at rest, and ask about data residency and key management for regulated environments.
One distinction worth keeping in mind: Encryption protects data if it's intercepted or accessed outside the system, but it doesn't limit what an authorized agent can see. Authorization controls determine whether an agent should access specific data in the first place. Both matter, and they solve different problems.
Approvals, policy gates, and least-privilege design
Not all AI agents function the same way. Agentic AI typically breaks into three action tiers: read, recommend, and execute. Most teams start with read-only before expanding.
- Read-only agents have minimal action risk. They surface information but don't change anything.
- Recommend-only agents carry more risk. Suggestions that are wrong or misleading can still influence decisions, so they need more oversight.
- Execute agents can change systems and act on behalf of employees. They typically require the tightest controls.
Most organizations have different team structures and AI requirements, so the right policy pattern will vary. That said, a few approaches work across most environments.
Step-up approvals require human sign-off before an agent moves forward on higher-risk actions. Dual control adds a second approver for sensitive workflows. An agent requesting database write access, for example, should require dual approval, while an agent accessing an employee record should be constrained to view-only unless explicitly approved for updates.
Blast-radius controls limit how much an agent can affect, which is especially useful during early deployment. When evaluating least-privilege agent design, look for scoped tool permissions rather than broad admin tokens. That design choice matters more than the marketing around it.
Be sure to validate your control model before live deployment. Dry runs should produce the same audit logs as production runs. If the logging behavior differs, you're testing a different system than the one you'll actually deploy.
Auditability, monitoring, and incident readiness
Auditability and monitoring rank among the most critical considerations when evaluating agentic AI platform security, especially if your team needs to support incident investigations or compliance reviews.
Logs need to include specific fields:
- Actor
- Request context
- Tool invoked
- Parameters
- Policy decision
- Approval chain
- Outcome
Tool invoked and parameters matter especially for agentic systems. Without them, logs can confirm that something happened without telling you what the agent actually did. All fields should be tamper-evident and time-stamped so security teams can reconstruct what happened, even months later.
If agent action logs can flow into your SIEM, your team gains visibility into both approved and denied actions. What the agent tried but didn't complete matters as much as what it succeeded at.
A few detection signals worth watching include unusual permission expansion requests, high-volume data access patterns, and repeated failed actions. An agent that attempts the same action multiple times before succeeding may be probing permission boundaries or running into a misconfigured policy gate.
During vendor evaluation, request a sample audit trail from a complete workflow. Make sure it includes the initial request, each step the agent took, policy decisions at each step, any approval chains, and the final outcome. That single trace tells you more about a platform's auditability than any architecture slide.
Evaluating agentic AI platform security
A structured evaluation framework does two things: keeps vendor assessments consistent and gives you something concrete to share with leadership. Start with your RFP, and organize questions into three buckets.
RFP security questions to ask
Security:
- How do you keep each organization's environment isolated?
- Has the platform undergone independent security testing?
Policy and control enforcement:
- How do you keep agents from getting more access than they need?
- Can audit logs be changed after they're created?
Operations:
- What steps does your team take if there's a security incident?
- How do you handle security updates for integrations and plugins?
- Does your platform support SIEM integration? If so, what log formats do you support, and how do we configure forwarding for agent action logs?
Aligning your RFP questions to OWASP and CSA categories also helps maintain consistency across vendors.
After the questions, build an evidence checklist. Ask for an architecture diagram, a sample audit log schema, a description of the identity and authorization model, data flow documentation, and a sample audit trail from a complete workflow. That last artifact often tells you more than everything else combined.
Scoring rubric
Apply weights across the three buckets:
- Security at 45%
- Policy and control at 35%
- Operations at 20%
Knowing what "good" looks like at each level matters as much as the percentages. For security, look for clear tenant isolation and least-privilege access. For policy and control, prioritize action-level audit trails and approval workflows at each action tier. For operations, SIEM integration is a key requirement for most regulated environments and worth prioritizing early in vendor evaluation.
Don't rush the rollout. Start with read-only workflows, validate the control model, then expand by autonomy tier as your team builds confidence.
Two practices may help reduce shadow AI pressure along the way: publishing a list of approved use cases so teams know what's available, and setting up a clear channel for requesting new workflows or plugins. When employees can see what's sanctioned and know how to request more, the incentive to reach for unsanctioned tools often drops significantly.
Deploy secure agentic AI platforms across your organization without reinventing governance
You've now seen what can go wrong when agents act across enterprise systems, and what controls help reduce those risks. The next question is which platform lets you move from evaluation to deployment without months of governance rework.
Evaluation often slows down when enterprises spend months assessing fragmented point solutions separately. Moveworks can help reduce that timeline with a prebuilt, governed platform built on a security-by-design foundation.
SOC 2 Type 2, ISO 27001, and FedRAMP authorized, Moveworks is built to enforce governance at the action level instead of asking you to bolt it on after deployment.
The Moveworks AI Assistant is the conversational interface where employees initiate requests. Agent Studio is the low-code builder for teams that need to go further, with hundreds of certified plugins, built-in access controls, approval workflows, and audit logging configured at the workflow level.
Together, those capabilities combine enterprise search with governed action, executing requests across enterprise systems within approval boundaries. That combination (enterprise search paired with governed execution) is what separates Moveworks from read-only assistants. Teams get answers and action in one platform.
With Moveworks, organizations can roll out AI agent strategies in weeks versus months. Audit trails and approval workflows built in from day one often mean teams have less reason to reach for unsanctioned tools. A governed path that works is usually the path employees take.
Explore the security controls behind the Moveworks platform.
Frequently Asked Questions
Secure typically means the platform is designed to protect systems and data from credible threats while supporting controlled operation. For agentic platforms, it often expands beyond confidentiality to include integrity of actions, governance, and traceability of decisions. Because agents can take actions, secure also implies strong authorization, policy checks, and audit logs. In regulated environments, you may also evaluate data residency, retention, and evidence that controls work in production.
It often means you can deploy agents with defined permission boundaries, predictable behavior under policy, and clear accountability for every action. Security teams typically look for least-privilege access, strong isolation, and monitoring that supports investigations. Many regulated teams also need documented processes for change control, incident response, and vendor risk management. A practical way to assess this is to validate controls through artifacts and pilot tests.
You can describe it as a governed agent platform that supports controlled tool access, auditable actions, and policy-based decisioning. This phrasing pushes the conversation toward measurable controls and evidence. It also helps align security, procurement, and operations on what needs to be validated. In many organizations, that shared definition is a key step toward safe deployment.
OWASP has projects that document common security risks for LLM and agent-like systems, and is often referenced as a way to categorize major agentic risks. These lists can help you structure a threat model around issues like injection, data exposure, excessive autonomy, and insecure tool use. They are best used as a starting taxonomy, then translated into platform requirements and proof artifacts you can request. Security teams often pair this with internal control frameworks and vendor risk processes.
A strong approach often combines data minimization, scoped tool permissions, policy gates, and monitoring. You may also evaluate how the platform handles sensitive data in prompts, logs, and memory, and whether DLP patterns can apply to inputs and outputs. In pilots, test realistic workflows like HR onboarding or access provisioning and verify where data flows at each step. Make sure audit logs are detailed enough to reconstruct what the agent accessed and why.
