Autonomous Actions Carry Financial Exposure
A single manipulated instruction can redirect an agent from its approved task. Without controls on what agents can do, one error can become a costly loss.
Adapt AI Agent Governance to Securely Accelerate Business Workflows
AI agents plan, call tools, and act across your systems at machine speed. Walled AI governs agents in real time to eliminate risk and enable smooth business operations.
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AI Agent Governance Is a Serious Concern Today
An AI agent can approve a payment, change a customer record, or send a message in seconds, often without a person reviewing the step. When an agent acts, your organisation carries the consequence.
Regulators Expect Clear Accountability
Frameworks such as the EU AI Act and ISO/IEC 42001 call for documented risk management, logging, and human oversight of autonomous systems. Leadership needs evidence of control, not intent.
Governance Is What Lets Teams Scale
Teams hold back when the risk of an agent is unclear. Clear boundaries let your people deploy agents with confidence and move faster across operations.
AI Agents Multiply Risk and Exposure
Agents widen your attack surface in ways traditional software does not.
What Effective Agent Governance Must Cover
Governance is a set of controls, not a single tool. Each control answers a question that every AI agent in your organisation must answer.
01
Inventory and Ownership
Know every agent, who owns it, and what it is for.
02
Identity and Access
Scoped, short-lived credentials and only the permissions a job needs.
03
Tool Controls
Every tool call checked against the agent's declared goal before it runs.
04
Runtime Guardrails
Real-time checks for injections, sensitive data and policy breaches.
05
Audit Trails
Every action logged in full, so every decision can be proven.
06
Human Oversight
Human approval for high-impact or irreversible actions.
Walled AI Secures Agent Interactions Both In and Out
Walled AI screens every interaction in real time, across agentic workflows, so unsafe inputs and outputs are stopped at the point of risk.
Input Controls
- Prompt injection and jailbreak detection
- Unauthorised-use and scope-violation filtering
- Goal-integrity checks on agent instructions
- Toxic and harmful content screening
- Data leakage prevention
- Custom business rule enforcement
Output Controls
- Toxic and harmful response blocking
- Hallucination flagging
- Data exfiltration and system-prompt leak detection
- Brand safety and tone compliance
- Regulatory language enforcement
- PII leakage detection in responses
Reference Architecture for Secure Agentic AI
Every agentic request and response passes through a single, governed pipeline before it reaches your people or systems. Input controls screen what agents receive, output controls check what they return, and logging records it all.
Input layers
Output layers
Input Governance
Screen every request, document and tool output before it reaches the model.
Policy and Planning Guardrails
Check the agent's plan against its declared goal and your policies.
Scoped Execution
Expose only the tools and data each agent needs, with sensitive data redacted before it is shared.
Output Validation and Egress Control
Validate responses and block unapproved data from leaving your boundary.
Across all layers
Full-Fidelity Logging
Record every decision and action so the full chain can be reviewed.
Aligned With the Regulations That Matter
European Union
EU AI Act
High-risk obligations cover risk management, logging and human oversight of AI systems.
United States
NIST AI RMF
A risk framework built on four functions: govern, map, measure and manage.
International
ISO/IEC 42001
Requires documented controls over autonomous decision-making.
United Kingdom
UK GDPR and DUAA 2025
Safeguards for automated decision-making (Articles 22A to 22D) and ICO accountability duties.
Singapore
PDPA and MAS TRM
Personal data protection, plus technology risk governance for regulated financial institutions.
India
DPDP Act 2023
Requires security safeguards and breach notification, with penalties for non-compliance.
Secure AI Agents Without Slowing Your Team Down
Governance should not slow your teams down. Walled AI keeps response latency low across every use case, so protection never becomes a bottleneck.
Performance comparison across different use cases: Response Latency (ms), Threat Detection (req/sec), Accuracy (%)
Response LatencyThreat DetectionAccuracy
Agent Governance Success Stories
Learn More About Agent Governance
Agentic AI Security: How to Govern and Secure Autonomous AI Agents
The six-layer reference architecture, governance controls and five starting steps.
Walled Protect: Input and Output Guardrails
Real-time screening of prompts, responses and agent instructions.
Enterprise RBAC
Department-level controls over which teams use which models and data.
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Frequently Asked Questions
What does AI agent governance cover?
It covers which agents exist, who owns them, what data and tools they can reach, how their inputs and outputs are checked, and how every action is logged.
How is agent governance different from access control?
Access control decides who can reach a model. Governance decides what an agent is allowed to do with it, on every interaction.
Does governance add latency?
Walled AI runs inline with your AI pipeline with sub-30ms processing latency, so users see no perceptible delay.
Can governance run on our own infrastructure?
Yes. Walled AI can be deployed on-premises, in a private cloud or in air-gapped environments.
Which threats does agent governance address?
It addresses prompt injection, goal hijacking, unauthorised use, multi-turn manipulation, data leakage and sensitive data leaving the organisation through agent outputs or tool calls.
What is goal hijacking in agentic AI?
Goal hijacking is when a malicious instruction hidden in a document, webpage or tool response redirects an autonomous agent away from its intended task. Governance checks each action against the agent's declared goal before it runs.
Can governance stop agents giving unlicensed advice?
Yes. Custom policy rules can block specialised advice an agent is not licensed or approved to give, such as investment, medical or legal recommendations, however the request is phrased.
Where should we start?
Begin by inventorying your agents and their owners, adding human approval for high-impact actions, replacing broad credentials with scoped, short-lived tokens, and filtering data before it leaves your boundary.