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Security for Agentic Workflows: Practical Controls
Agentic workflows—where AI systems plan steps, call tools, and take action—are moving quickly from pilots into production environments. With that shift comes a change in the security model.
Unlike traditional automation, agents are context-driven and probabilistic. They reason over inputs, decide which tools to use, and may chain multiple actions together. This flexibility is what makes them powerful—but it also introduces new security considerations.
This article outlines the key security risks in agentic workflows and the practical controls enterprises are using to manage them, without slowing delivery.
The Core Security Risks in Agentic Workflows
Most agent security incidents are not caused by malicious intent or “runaway AI.” They stem from unclear boundaries, excessive permissions, or missing governance.
1. Prompt Injection
What it is
Prompt injection occurs when untrusted input influences agent behavior in unintended ways—causing the agent to ignore instructions, reveal information, or misuse tools.
Common entry points
- Customer messages
- Support tickets
- Emails
- Documents ingested for retrieval
Because agents reason over text, they may treat malicious instructions as legitimate context unless explicitly constrained.
2. Tool Misuse
What it is
Agents rely on tools—APIs, databases, SaaS platforms—to perform actions. If tools are broadly accessible or poorly scoped, agents can execute actions beyond their intended role.
Examples
- Writing data when only reading was expected
- Triggering downstream workflows unintentionally
- Using admin-level APIs for routine tasks
This is usually a design issue, not an intelligence issue.
3. Data Leakage
What it is
Agents may expose sensitive data through responses, logs, or external calls if retrieval and output boundaries are not clearly defined.
Typical leakage paths
- Overly broad document search
- Including raw data in logs
- Sending sensitive fields to external tools
This risk increases when agents combine internal and external data sources.
4. Privilege Escalation
What it is
Privilege escalation happens when an agent gains broader access through a chain of actions—each seemingly valid on its own.
Why it’s dangerous
- No single step looks risky
- The issue only appears across multiple tool calls
- Traditional permission reviews may miss it
Practical Controls That Reduce Risk
Securing agentic workflows does not require reinventing security practices. It requires applying existing principles consistently and explicitly.
1. Allowlisted Tools Only
Agents should only be able to call explicitly approved tools.
Best practices:
- No dynamic tool discovery
- Clear purpose for each tool
- Separate tool lists for dev, test, and prod
If a tool is not allowlisted, the agent cannot access it—by design.
2. Scoped Permissions
Agents should use least-privilege access, just like service accounts.
Examples:
- Read-only access for analysis agents
- Write access limited to specific objects
- No shared credentials across agents
Avoid granting broad “admin” permissions for convenience.
3. Retrieval Boundaries
Retrieval-augmented agents should operate within clear data boundaries.
Controls include:
- Approved document sources only
- Restricted indexes or folders
- Filters by owner, classification, or time range
Retrieval is a security boundary, not just a relevance feature.
4. Policy Checks Before Action
Sensitive actions should be gated by deterministic checks, such as:
- Thresholds (amounts, counts, impact)
- Role or policy validation
- Required approvals
Where possible, these checks should live outside the model and be enforced programmatically.
5. Immutable Logging
Every agent action should be logged with:
- Inputs and outputs (with redaction)
- Tool calls and parameters
- Approval decisions
- Timestamps and identifiers
Logs should be:
- Write-once
- Tamper-resistant
- Retained according to compliance needs
If actions cannot be reconstructed, they cannot be audited or secured.
6. Manual Approvals for High-Risk Actions
Human-in-the-loop controls are a feature, not a weakness.
Use manual approval for:
- Financial transactions
- Permission changes
- Data deletion
- Customer-impacting actions
This ensures accountability while allowing agents to handle low-risk work autonomously.
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A 1-Day Red Team Test Plan for Agentic Workflows
Before promoting an agent to production, a short, focused red team exercise can uncover most critical issues.
Morning: Threat Modeling (2 hours)
- Inventory agent tools and permissions
- Identify sensitive data paths
- Define unacceptable outcomes
Midday: Adversarial Testing (3 hours)
- Attempt prompt injection via inputs
- Test tool misuse scenarios
- Probe retrieval boundaries
- Try to induce data leakage
Afternoon: Review & Hardening (2–3 hours)
- Review logs and traces
- Identify permission gaps
- Add missing approvals or policy checks
- Document findings and fixes
This exercise is lightweight, repeatable, and often more effective than theoretical reviews.
Common Security Mistakes to Avoid
- Giving agents broad API tokens
- Relying on model behavior instead of controls
- Logging everything without redaction
- Treating security as a post-launch step
- Skipping adversarial testing
Agent security failures are almost always design failures, not AI failures.
Final Thought
Agentic workflows don’t introduce entirely new security problems—but they do demand more intentional boundaries. When tools are allowlisted, permissions are scoped, actions are logged, and approvals are explicit, agents can be safer than many traditional integrations.
Security that enables controlled autonomy will always scale better than security that tries to block progress.
Agent security is defined by boundaries, not by trust in the model.
To help teams apply these controls, we’ve created a Security Checklist for Agentic Workflows, including a one-day red team test template.
Request the checklist to review your current or planned agent deployments.
If you’d like help assessing your agent architecture or running a red team exercise, contact us to set up a focused security review.
