@@ -11,6 +11,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ### Added
 - **Model Zoo Benchmark**: Introduced a comprehensive scientific validation notebook (`examples/trustlens_model_zoo_benchmark.ipynb`) that systematically evaluates TrustLens across 6 model architectures and multiple data corruption scenarios with statistical aggregation.
+- **Deployment Recommendation Explanations**: Added `TrustReport.deployment_explanation` and `TrustReport.deployment_summary` to provide structured deployment verdict explanations, identify primary risks, and surface actionable recommendations based on Trust Score penalties and sub-scores.
 
 ### Fixed
 - Fixed incorrect `top_mistake_indices` in `misclassification_summary()` to return **global dataset indices** instead of local filtered subset positions, improving downstream EDA and debugging workflows for high-confidence model errors. (PR #104) Thanks @dicnunz 🙌
 
@@ -97,6 +97,12 @@ def main():
     print("\n[4/4] Report Summary:")
     report.show()
 
+    print("\n" + "=" * 40)
+    print("DEPLOYMENT RECOMMENDATION EXPLANATION")
+    print("=" * 40)
+    print(report.deployment_summary)
+    print("=" * 40 + "\n")
+
     # Visualize fairness deep-dive
     print("\n[*] Generating fairness summary plot...")
     report.plot_bias(mode="summary", save_path="examples/output/quickstart_bias.png")
 
@@ -0,0 +1,141 @@
+import numpy as np
+import pytest
+
+from trustlens.report import TrustReport
+from trustlens.trust_score import TrustScoreResult
+
+
+@pytest.fixture
+def mock_report():
+    """Create a minimal TrustReport with empty metrics to inject test states."""
+    y_true = np.array([0, 1, 0, 1])
+    y_pred = np.array([0, 1, 0, 1])
+    y_prob = np.array([[0.9, 0.1], [0.1, 0.9], [0.9, 0.1], [0.1, 0.9]])
+
+    return TrustReport(
+        results={}, model=None, X=np.zeros((4, 2)), y_true=y_true, y_pred=y_pred, y_prob=y_prob
+    )
+
+
+def test_deployment_explanation_pass(mock_report):
+    # Mock an "A" grade TrustScoreResult with no penalties
+    mock_report.trust_score = TrustScoreResult(
+        score=95,
+        grade="A",
+        verdict="High Trust",
+        sub_scores={"calibration": 90.0, "failure": 95.0, "bias": 95.0},
+        penalties_applied={},
+        is_blocked=False,
+    )
+
+    exp = mock_report.deployment_explanation
+
+    assert exp["verdict"] == "PASS"
+    assert len(exp["recommendations"]) == 1
+    assert "meets all trustworthiness criteria" in exp["recommendations"][0]
+
+    # All reasons should be pass
+    for r in exp["reasons"]:
+        assert r["status"] == "pass"
+        assert "assessment completed" in r["message"]
+
+    assert exp["primary_risk"] is not None
+    assert exp["primary_risk"]["metric"] in ["Calibration", "Failure", "Fairness"]
+
+
+def test_deployment_explanation_caution_with_multiple_penalties(mock_report):
+    mock_report.trust_score = TrustScoreResult(
+        score=65,
+        grade="B",
+        verdict="Good Trust",
+        sub_scores={"calibration": 80.0, "failure": 75.0, "bias": 70.0},
+        penalties_applied={"Fairness": 10.0, "Calibration": 5.0},
+        is_blocked=False,
+    )
+
+    exp = mock_report.deployment_explanation
+
+    assert exp["verdict"] == "CAUTION"
+    assert len(exp["recommendations"]) == 2
+
+    # Check reasons
+    fail_reasons = [r for r in exp["reasons"] if r["status"] == "fail"]
+    assert len(fail_reasons) == 2
+    assert any("Fairness penalty applied" in r["message"] for r in fail_reasons)
+    assert any("Calibration penalty applied" in r["message"] for r in fail_reasons)
+
+    pass_reasons = [r for r in exp["reasons"] if r["status"] == "pass"]
+    assert len(pass_reasons) == 1
+    assert "Failure assessment completed" in pass_reasons[0]["message"]
+
+    # Primary risk must be the one with the highest penalty (Fairness)
+    assert exp["primary_risk"]["metric"] == "Fairness"
+    assert exp["primary_risk"]["value"] == 10.0
+
+
+def test_deployment_explanation_block(mock_report):
+    mock_report.trust_score = TrustScoreResult(
+        score=20,
+        grade="D",
+        verdict="Low Trust",
+        sub_scores={"calibration": 20.0, "failure": 30.0},
+        penalties_applied={"Failure": 15.0},
+        is_blocked=True,
+    )
+
+    exp = mock_report.deployment_explanation
+    assert exp["verdict"] == "BLOCK"
+    assert exp["primary_risk"]["metric"] == "Failure"
+    assert len(exp["recommendations"]) == 1
+    assert "Inspect high-confidence misclassifications" in exp["recommendations"][0]
+
+
+def test_deployment_explanation_missing_modules(mock_report):
+    # What if only calibration was evaluated?
+    mock_report.trust_score = TrustScoreResult(
+        score=55,
+        grade="C",
+        verdict="Moderate Trust",
+        sub_scores={"calibration": 55.0},
+        penalties_applied={"Calibration": 12.0},
+        is_blocked=False,
+    )
+
+    exp = mock_report.deployment_explanation
+    assert len(exp["reasons"]) == 1
+    assert exp["reasons"][0]["message"] == "Calibration penalty applied"
+    assert len(exp["recommendations"]) == 1
+
+
+def test_deployment_summary_formatting(mock_report):
+    mock_report.trust_score = TrustScoreResult(
+        score=75,
+        grade="C",
+        verdict="Moderate Trust",
+        sub_scores={"calibration": 90.0, "bias": 60.0},
+        penalties_applied={"Fairness": 15.0},
+        is_blocked=False,
+    )
+
+    summary = mock_report.deployment_summary
+    assert "Deployment Verdict: CAUTION" in summary
+    assert "✓ Calibration assessment completed" in summary
+    assert "✗ Fairness penalty applied" in summary
+    assert "Primary Risk:\nFairness" in summary
+    assert "• Investigate subgroup performance disparities" in summary
+
+
+def test_serialization_includes_explanation(mock_report):
+    mock_report.trust_score = TrustScoreResult(
+        score=95,
+        grade="A",
+        verdict="High Trust",
+        sub_scores={"calibration": 90.0},
+        penalties_applied={},
+        is_blocked=False,
+    )
+
+    flat_dict = mock_report.to_dict()
+    assert "deployment_verdict" in flat_dict
+    assert flat_dict["deployment_verdict"] == "PASS"
+    assert "deployment_primary_risk_metric" in flat_dict
@@ -125,6 +125,93 @@ def _format_score_explanation(self) -> list[str]:
                 lines.append(f"  - {label:<16}: {name} (-{val:.1f})")
         return lines
 
+    @property
+    def deployment_explanation(self) -> dict[str, Any]:
+        """Provide a structured explanation for the deployment verdict."""
+        ts = self.trust_score
+        grade_map = {"A": "PASS", "B": "CAUTION", "C": "CAUTION", "D": "BLOCK"}
+        verdict = "BLOCK" if ts.is_blocked else grade_map.get(ts.grade, "PASS")
+
+        reasons = []
+        recommendations = []
+        penalties = getattr(ts, "penalties_applied", {})
+
+        # Recommendations mapping
+        rec_map = {
+            "calibration": "Consider probability calibration techniques such as temperature scaling or isotonic regression before deployment.",
+            "fairness": "Investigate subgroup performance disparities and consider fairness constraints before deployment.",
+            "failure": "Inspect high-confidence misclassifications and verify confidence-weighted error distributions.",
+            "representation": "Review latent embedding quality; poor representation may indicate failure to capture fundamental class separation.",
+        }
+
+        # Map dimension to standard penalty name logic
+        dim_to_penalty_key = {
+            "calibration": "Calibration",
+            "failure": "Failure",
+            "bias": "Fairness",
+            "representation": "Representation",
+        }
+
+        # Case-insensitive penalty key matching
+        penalties_lower = {k.lower(): (k, v) for k, v in penalties.items()}
+
+        for dim in self.trust_score.sub_scores.keys():
+            expected_penalty_key = dim_to_penalty_key.get(dim, dim.title())
+
+            if expected_penalty_key.lower() in penalties_lower:
+                actual_key, _ = penalties_lower[expected_penalty_key.lower()]
+                reasons.append({"status": "fail", "message": f"{actual_key} penalty applied"})
+                # Map back to our recommendation keys
+                rec_key = "fairness" if dim == "bias" else dim
+                if rec_key in rec_map:
+                    recommendations.append(rec_map[rec_key])
+            else:
+                reasons.append(
+                    {"status": "pass", "message": f"{expected_penalty_key} assessment completed"}
+                )
+
+        if not recommendations:
+            recommendations.append("Model meets all trustworthiness criteria for deployment.")
+
+        # Determine primary risk
+        primary_risk = None
+        if penalties:
+            # Largest penalty
+            matched_key = max(penalties.items(), key=lambda x: x[1])[0]
+            primary_risk = {"metric": matched_key, "value": penalties[matched_key]}
+        elif ts.sub_scores:
+            # Lowest sub-score
+            lowest_dim = min(ts.sub_scores.items(), key=lambda x: x[1])[0]
+            primary_risk = {
+                "metric": dim_to_penalty_key.get(lowest_dim, lowest_dim.title()),
+                "value": ts.sub_scores[lowest_dim],
+            }
+
+        return {
+            "verdict": verdict,
+            "reasons": reasons,
+            "primary_risk": primary_risk,
+            "recommendations": recommendations,
+        }
+
+    @property
+    def deployment_summary(self) -> str:
+        """Format the deployment explanation into a human-readable string."""
+        exp = self.deployment_explanation
+        lines = [f"Deployment Verdict: {exp['verdict']}", "", "Reasons:"]
+        for reason in exp["reasons"]:
+            icon = "✗" if reason["status"] == "fail" else "✓"
+            lines.append(f"{icon} {reason['message']}")
+
+        if exp["primary_risk"]:
+            lines.extend(["", "Primary Risk:", str(exp["primary_risk"]["metric"])])
+
+        lines.extend(["", "Recommendations:"])
+        for rec in exp["recommendations"]:
+            lines.append(f"• {rec}")
+
+        return "\n".join(lines)
+
     # ------------------------------------------------------------------
     # Internal helpers
     # ------------------------------------------------------------------
@@ -1242,6 +1329,7 @@ def save(self, path: str = "trust_report", **kwargs) -> Path:
                 "trust_score": self.trust_score.score,
                 "grade": self.trust_score.grade,
                 "sub_scores": self.trust_score.sub_scores,
+                "deployment_explanation": self.deployment_explanation,
             }
             p.write_text(json.dumps(data, indent=2), encoding="utf-8")
             logger.info("Unified Report JSON saved to: %s", p)
@@ -1281,6 +1369,7 @@ def save(self, path: str = "trust_report", **kwargs) -> Path:
                     "sub_scores": ts.sub_scores,
                     "weights_used": ts.weights_used,
                     "breakdown": ts.breakdown,
+                    "deployment_explanation": self.deployment_explanation,
                 },
                 indent=2,
             ),
@@ -1328,6 +1417,16 @@ def to_dict(self) -> dict[str, Any]:
         flat["framework"] = self.metadata.get("framework", "unknown")
         flat["trustlens_version"] = self.metadata["trustlens_version"]
 
+        # Flatten deployment explanation for MLflow/W&B tracking
+        exp = self.deployment_explanation
+        flat["deployment_verdict"] = exp["verdict"]
+        flat["deployment_primary_risk_metric"] = (
+            exp["primary_risk"].get("metric") if exp["primary_risk"] else None
+        )
+        flat["deployment_primary_risk_value"] = (
+            exp["primary_risk"].get("value") if exp["primary_risk"] else None
+        )
+
         for dim, score in self.trust_score.sub_scores.items():
             flat[f"trust_{dim}_score"] = score
         return flat