Autonomy Without Oversight = Exposure

Agents don't wait for a human to approve every step — and neither should your security. Prediction Guard enforces policy at the moment of action: every model call, every tool invocation, every piece of data an agent touches is checked against your rules in real time, not reviewed after the fact. Deny-by-default. No standing exceptions. No action goes through unchecked.

Deny-by-Default Policy Enforcement, on Every Action

Every model interaction, tool call, and data access request is evaluated against your governance policy before it executes (not sampled, not logged for later review). If an action isn't explicitly permitted, it doesn't happen.

Why it matters: In the July 2026 Hugging Face intrusion, the compromised agent never had to break a rule. It exploited the absence of one. It stayed narrowly focused on its assigned task while violating every unstated boundary around it. Deny-by-default closes that gap: an agent can't wander into unauthorized territory that was simply never denied.

Custom Policies, or Industry Frameworks Out of the Box

Define your own governance rules, or enforce established frameworks like NIST 600-1, OWASP recommendations, and the EU AI Act directly at runtime. Policies apply uniformly across every model, every provider, and every agent in your AI system.

Why it matters: Compliance shouldn't be a quarterly audit that discovers what already went wrong. When frameworks are enforced live, violations are prevented, not just documented after the damage is done.

Least Agency, Enforced at the Tool Level

Beyond least privilege for users and systems, Prediction Guard restricts the scope of what each individual MCP server can do (a database tool gets read-only queries, an email tool gets no send rights unless explicitly scoped, etc.).

Why it matters: Traditional access controls can't stop an agent from misusing tools it's technically authorized to use. Least agency constrains the action itself, not just the account behind it.

An Immutable Log of Every AI Security Event

Every policy decision, blocked action, and governance violation is written to append-only, tamper-resistant storage. No single point of failure (or single compromised credential) can alter the record. Every entry ties back to a specific agent identity, timestamp, and triggering request.

Why it matters: During the Hugging Face incident, defenders relied on reconstructing over 17,000 recorded events to separate real attacker activity from decoys and establish what actually happened. That reconstruction is only trustworthy if the log itself couldn't have been altered by the same actor who compromised the environment. An immutable trail means your forensic record survives the incident it's documenting (and holds up for compliance audits, regulatory review, and legal proceedings, not just internal investigation).

Governance Baselines

Quickly align your governance enforcement to frameworks like NIST AI RMF, NIST 600-1, OWASP LLM Top 10, and OMB M-26-04.

PII Processing

Prevent unauthorized disclosure of PII. Block or log when PII flows into or out of models or MCP servers. Remediate these instances by masking, faking, or replacing PII within user inputs or AI outputs.

Prompt Injection Detection

Detect and block jailbreaking, direct prompt injection, and indirect prompt injection attacks across every model and agent in the system.

Handle Unsafe AI Outputs

Enforce the detection and logging of unsafe model outputs, including toxicity, lack of grounding, malicious URLs, and system prompt leakage.

Immutable Audit Log

Maintain a tamper-proof log of every governance violation with attribution information, timestamps, and input/output data for compliance.

SIEM / SOAR Integration

Stream structured AI events to Splunk, Grafana, Datadog, or any other SIEM/SOAR system used within your organization.

An AI governance platform is software that enforces policy, tracks risk, and produces audit-ready evidence across an organization's AI systems, models, and agents. Most platforms route AI traffic to external infrastructure to apply policy. The enforcement decision and the resulting audit log both leave your environment.

Prediction Guard runs as a self-hosted control plane instead. Governance policies are enforced at runtime, before a model call completes, and the resulting audit logs are generated inside your own infrastructure and consumed by your SIEM.

Strong AI governance tools combine a full inventory of the models, agents, and tools in use with active policy enforcement and structured audit logging, not just after-the-fact monitoring dashboards.

Prediction Guard covers all three: AI System registration for inventory, real-time enforcement against NIST AI RMF, OWASP, and AIUC-1 policies, and audit logs that forward natively into Splunk, Datadog, or any SIEM your security team already uses.

AI governance software focuses on policy enforcement, regulatory alignment, and audit evidence, while AI security software focuses on defending against attacks like prompt injection or data poisoning. In practice, enterprises need both working together.

Prediction Guard enforces governance policy and security controls, such as PII protection and prompt injection detection, from the same self-hosted control plane, so the two are never stitched together as separate tools.

Many AI governance solutions operate as external gateways that route AI traffic outside an organization's own infrastructure before applying policy.

Prediction Guard deploys entirely inside the customer's environment, self-hosted, in a cloud VPC, or air-gapped, so governance enforcement and audit logs never leave the organization's security boundary.