Policy Coverage
Where to improve
- What attack paths remain uncovered?
- What controls can still be bypassed?
- What new threats or environment changes create gaps?
- Have we comprehensively closed the failure?
01You blocked
Find the gaps. Optimize the defense. Learn from every outcome.
You blocked
the attack.
Did you close
the failure?
A blocked attack tells you one path was covered. It does not tell you whether the same unsafe outcome is still reachable through a different tool, another agent, a poisoned document, or a longer chain. Silex starts from that real incident and finds out.
Find gapsOptimizeValidateLearn
02One path covered.
One blocked attack
One path covered.
Four still open.
A finance agent reads an email carrying a prompt injection. It calls a vendor-update tool and attempts to change a bank account. Your existing guardrails block it. The ticket closes. Everyone moves on.
Silex starts exactly there — taking the attack input, the execution context, the agent, tools and permissions involved, where the block happened, and the surrounding logs, then reconstructing a model of the environment. And it asks the question nobody asked:
Was that one path, or the only path?
The block was real. Other routes to the same unsafe outcome were still open.
One guardrail covered path 1. The other four were never tested.
Malicious input
Prompt injection in an email
- 1Blocked routeObservedCoveredFinance agentVendor-update tool
- 2Alternate toolObservedNever testedFinance agentInvoice API
- 3Agent delegationLatentNever testedProcurement agentFinance agent
- 4Indirect dataLatentNever testedFinance agentReads shared document
- 5Multi-stepLatentNever testedFinance agentEmail toolVendor-update tool
Unsafe outcome
Bank account change
Every path carries its evidence grade
- Observed
- The route was exercised and appears in your runtime graph.
- Latent
- The route is possible by type over your real environment — classes, inherited permissions and constraints allow it, with no trace behind it.
- Declared
- The route is possible by type over an agent's configuration, before it has run at all.
A latent path is a hypothesis with an address: it names the class and constraint that generated it, so you can dismiss it from your own knowledge. It is never presented with the confidence of a route we watched being taken.
Then it finds the best policy — not just more rules.
Silex generates policy options at different control points, validates them from simulation through shadow and canary before production, and ranks the set that closes the paths with the least business friction. In this illustration:
- 1Restrict the invoice API for the finance agent
- 2Require re-authentication for any vendor change, given the action and its context
- 3Add content validation for shared documents
- 4Reduce agent permissions to the minimum necessary
A canonical illustration of how the flow works — not a customer incident, and not a measured result. The same structure applies to data exfiltration, privilege escalation, and agent-to-agent attacks.
03Two jobs,
The two jobs
Two jobs,
one loop.
- Coverage
- tells us where to improve.
- Optimization
- tells us how to improve.
- Real-world outcomes
- teach us what works.
Coverage without optimization is a longer list of controls. Optimization without coverage is tuning the defenses you happened to think of.
Silex AI is the self-evolving security intelligence layer for continuous policy coverage and optimization.
One observed
pathEverything
still
reachable
pathEverything
still
reachable
These are the questions the product exists to answer. If you cannot answer them about your own environment today, that is the gap.
Policy Optimization
How to improve
- Which policy change is best?
- How much risk does it reduce?
- What new risks does it introduce?
- What are the false-positive and operational costs?
- Does it satisfy compliance and business constraints?
- Is there a better alternative?
04From one incident
How it works
From one incident
to optimized policy.
Five stages, running continuously. Two of them — finding coverage gaps and generating policy alternatives — are the ones most tools skip entirely.
- 1
Observed Incident (from production)
- Observe
A real attack is detected and blocked by your existing guardrails. We take the attack input, the execution context, the agent, tools and permissions involved, where the block happened, and the surrounding logs.
- 2
Reconstruct & Model the Environment
- Understand
We build a high-fidelity model of your environment through the Security Ontology — agents, tools and APIs, data and assets, policies and controls, and business context, modeled together so paths through them can be reasoned over.
- 3
Explore Attack Paths & Policy Options
- Find Coverage Gaps
- Generate Policy Alternatives
Starting from the observed failure, we systematically explore the alternative attack paths and evaluate multiple policy options at different control points.
- Graph-based path exploration
- Adversarial LLM agents generating variants
- Constraint-aware simulation respecting real permissions and logic
- Causality analysis to identify critical dependencies
- Multiple policy options, not block-versus-allow
- 4
Validate & Optimize Policies
- Optimize
- Validate
Each candidate policy is tested in simulation and in real environments, and scored across all six objectives — risk reduction, coverage, business friction, compliance, cost, performance. What comes back is ranked alternatives with expected impact and confidence: the best policy, not simply more rules.
- 5
Recommend & Continuously Learn
- Deploy
- Measure
- Learn
We recommend ranked policy changes, deploy them through the enforcement surface you already own, and compare real-world outcomes against the predicted ones.
Real-world outcomes update the knowledge base.
As your agents, tools, permissions, business context, and threat landscape change, the coverage picture, the causal model, and the simulation environment change with them.
05Nothing reaches
Validate & optimize
Nothing reaches
production unproven.
Self-evolution means governed self-improvement — not a model rewriting production policy the moment it sees an anomaly. Every candidate climbs the same ladder, no stage skipped.
- Stage 1
Simulation
Every path and policy option is tested against a model of your environment that respects real permissions and logic.
- Stage 2
Shadow
The candidate is replayed against real traffic without acting on it.
- Stage 3
Canary
Enabled for a limited scope, with real outcomes measured against the prediction.
- Stage 4
Production
Full deployment, reached only after every prior stage passes, with rollback preserved.
Scored across six objectives.
Not security alone, and not security versus the business. Every candidate policy is evaluated on all six at once.
- Risk reduction
- Coverage
- Business friction
- Compliance
- Cost
- Performance
The output is a set of ranked alternatives — each with its rationale, expected impact, supporting evidence, and a confidence level. Not a single recommended fix.
An intelligence layer — not an enforcement point.
The selected policy is deployed through the enforcement surface you already own and have already audited. Silex does not take custody of your production traffic, your credentials, or the final blocking decision. The agents find gaps and rank hypotheses; validation and measurement decide.
Agentic security by design
- Least privilege
- Sandboxing
- Policy integrity
- Human approval
- Provenance
- Runtime monitoring
- Rollback
06What makes Silex
What’s different
What makes Silex
different.
Everyone executes rules efficiently. Nobody finds what those rules still leave open, then proves which replacement is best.
Coverage and Optimization Together
Others stop at finding gaps, or at enforcing rules. Silex finds what is still uncovered, then generates and ranks alternatives with rationale, expected impact, evidence, and confidence. Not more rules — the best policy.
Causal Security Reasoning
Explain why an attack got as far as it did, and predict what a policy change would do — rather than relying only on correlations, rules, or risk scores. The predictive half is the differentiated one.
Sim-to-Real Validation
Prove effectiveness through simulation, shadow, canary, and production — measured against real-world outcomes rather than simulation alone.
Business-Aware Recommendations
Respect risk appetite, compliance, cost, and operational constraints. A control that stops the attack and also stops the business is not a valid recommendation.
Self-Evolving Intelligence
Compare predicted against observed outcomes and use the gap as evidence — re-evaluating as agents, tools, permissions, workloads, business requirements, and threats change.
Secure Agentic Harness
Govern the AI that governs security: least privilege, sandboxing, policy integrity, human approval, provenance, runtime monitoring, and rollback. Rollback is scoped to agent-side state — business effects get a compensating-action recommendation, executed through your systems.
Under the hood
- Security Ontology
- The environment graph — agents, tools and APIs, data and assets, policies and controls, and business context, modeled together so paths through them can be reasoned over.
- Causal Foundation
- Models why defenses work or fail, not merely what co-occurs.
- Business Harness
- Bounds decisions by risk appetite, compliance, asset criticality, usability, cost, and operational requirements.
- Secure Agentic Harness
- Governs the security agents themselves.
- Self-Evolving Security Knowledge Base
- The accumulating record of coverage findings, predictions, observations, and policy effectiveness.
The moat is not a frontier model. It is the Security Ontology, the Causal Foundation, and the accumulated coverage and policy-outcome record. See how it is built.
Where the adjacent categories stop
| Category | Where it stops | The step Silex takes |
|---|---|---|
| Enforcement and identity infrastructurefirewalls, IAM, gateways, guardrails | Where it stopsExecutes predetermined rules efficiently and at low latency. | The step Silex takesFinds what those rules do not cover, and optimizes which rules they should be. |
| Vulnerability management | Where it stopsEnumerates known vulnerabilities. | The step Silex takesFinds uncovered attack paths and bypassable controls — gaps, not CVEs. |
| Detection, SIEM, XDR | Where it stopsTells you something happened. | The step Silex takesTells you what is still reachable and which policy is best, then measures whether it held. |
| Breach-and-attack simulation, red teaming | Where it stopsShows a defense can be bypassed. | The step Silex takesGenerates ranked alternatives, validates them in staged production, and learns from the outcome. |
| Posture management, policy-as-code | Where it stopsChecks configuration against a fixed standard. | The step Silex takesFinds what the standard misses and optimizes the standard itself. |
| Linters and static analysis | Where it stopsFlag rule violations in a definition. | The step Silex takesReason by type about which unsafe outcomes a definition makes reachable, and name the control points — then confirm at runtime. |
| Security knowledge graphs | Where it stopsRepresent what exists and what is reachable. | The step Silex takesAdd causal laws on top, so the question becomes what a policy change would do — not merely what connects to what. |
| Root-cause and failure-tracing tools | Where it stopsExplain why an observed run failed. | The step Silex takesAlso predict what a change would do before it is made — the interventional half, not only the explanatory one. |
| AI and agent observability | Where it stopsExplains what a system did and why it failed. | The step Silex takesDecides what the policy should be next, under business constraints. |
| Evaluation platformsoutput quality and trajectory scoring | Where it stopsScore output quality and trajectory success. | The step Silex takesVerify reachability and enterprise state — a model swap can leave every output score unchanged while opening a path. |
| Governance and GRC platforms | Where it stopsQuestionnaires and self-attested evidence. | The step Silex takesNot our category. Compliance is one scored objective inside the Business Harness. |
07From enforcement
Who it’s for
Who it’s for.
Four different mandates, one shared pair of questions: what can our agents still reach, and which control is worth the friction it puts on the business?
Security teams (CISO, AppSec)
Prove and improve agent security posture.
AI and agent owners
Keep their agents safe and productive.
Risk and compliance
Demonstrate due diligence and meet regulatory requirements.
Enterprise IT
Integrate with existing security and governance.
Example use cases
Money movement
Prevent unauthorized bank account changes, payments, and financial fraud.
Data exfiltration
Stop unauthorized access to sensitive data.
Privilege escalation
Prevent agents from gaining excessive permissions.
Indirect prompt injection
Find and block attacks arriving through documents, emails, and web content.
Agent-to-agent attacks
Validate security in multi-agent environments.
From enforcement
to continuous learning.
We are moving enterprise security from static policy enforcement to continuously learning policy coverage and optimization.
- Prove you are actually safer. Go beyond a single blocked attack and understand your real risk exposure.
- Optimize, don't just add rules. The most effective policy with the least business disruption.
- Actionable recommendations. Concrete, validated policy updates — ranked, with evidence.
- Continuous improvement. Your environment evolves, and so does your security, with evidence.
Silex is an intelligence layer, not an inline enforcement point. The selected policy deploys through the enforcement surface you already own and have already audited — we never take custody of production traffic, credentials, or the final blocking decision.
Every cycle leaves behind coverage findings and a prediction-versus-outcome record. That is the asset that compounds.