Add readable redacted trace summaries

Co-Authored-By: Codex <codex@openai.com>

README.md

@@ -127,11 +127,14 @@ The checkbox is visible and enabled by default on each request, and users can
 turn it off before submitting.
 
 Trace creation is deterministic Python logic and makes no additional model
-request. It records a safe input type or image description, input category,
-urgency, fixed signals, and result counts. The input field uses a safe
-`text: ...` or `image: ...` description generated from fixed templates.
-It never stores raw or redacted messages, screenshots, links, identifiers,
-model explanations, reply text, exceptions, or credentials.
+request. Text inputs are aggressively redacted and capped at 500 characters;
+images use a fixed `image: ...` description without OCR or image storage. The
+trace also records category, urgency, fixed signals, result counts, and a
+deterministic `result_summary` explaining the scam pattern and risk label.
+All trace columns are flat scalar values; no dataset cell contains a nested
+dictionary.
+It never stores raw messages, screenshots, links, detected identifiers, model
+explanations, reply text, exceptions, or credentials.
 
 Safe records are queued without blocking the response, written in batches of
 20 or after 60 seconds, and uploaded as unique JSONL shards. Hub failures leave

static/index.html

@@ -78,7 +78,7 @@
           <input id="saveTrace" type="checkbox" checked>
           <span>
             <strong>Publish privacy-safe trace</strong>
-            <small>No raw message, screenshot, link, identifier, or model text is stored.</small>
+            <small>Stores automated redacted text or an image description. Raw text, screenshots, links, identifiers, and model text are not stored.</small>
           </span>
         </label>
         <div class="form-actions">

tests/test_tracing.py

@@ -67,6 +67,12 @@ class TraceTests(unittest.TestCase):
         self.assertTrue(image_record["input"].startswith("image: "))
         self.assertEqual(image_record["input_category"], "unknown")
         self.assertFalse(image_record["urgency"])
+        self.assertIn(
+            "does not confirm a new scam type",
+            image_record["result_summary"],
+        )
+        self.assertIn("Strong scam indicators", image_record["result_summary"])
+        self.assertEqual(image_record["input_storage"], "image_description_only")
         for removed in (
             "pipeline_steps",
             "cache",
@@ -88,12 +94,21 @@ class TraceTests(unittest.TestCase):
             example_id="",
             assessment=None,
         )
-        self.assertEqual(
-            text_record["input"],
-            "text: Courier-style content with urgency, payment, courier signals",
-        )
+        self.assertTrue(text_record["input"].startswith("text: "))
+        self.assertIn("courier payment required today", text_record["input"])
+        self.assertNotIn("Urgent", text_record["input"])
         self.assertEqual(text_record["input_category"], "courier")
         self.assertTrue(text_record["urgency"])
+        self.assertIn("Known courier pattern", text_record["result_summary"])
+        self.assertIn(
+            "No completed assessment result was available",
+            text_record["result_summary"],
+        )
+        self.assertEqual(text_record["input_storage"], "redacted_text")
+        self.assertFalse(any(
+            isinstance(value, (dict, list))
+            for value in text_record.values()
+        ))
 
     def test_opt_out_does_not_queue_trace(self) -> None:
         with patch("app.queue_trace") as queue_mock:

traces/data/trace_samples.jsonl

@@ -1,6 +1,6 @@
-{"input": "text: Courier-style content with link, urgency, payment, courier signals", "input_category": "courier", "privacy": {"exception_text_stored": false, "identifiers_stored": false, "raw_image_stored": false, "raw_input_stored": false, "raw_model_output_stored": false}, "result": {"red_flag_count": 4, "reply_draft_policy": "suppressed", "reply_draft_returned": false, "risk_label": "Likely scam", "safe_next_step_count": 4}, "signals": {"account_threat": false, "challan": false, "cnic": false, "courier": true, "credentials": false, "link": true, "otp": false, "payment": true, "refund_or_prize": false}, "timestamp": "2026-06-07T06:37:30.001011+00:00", "trace_id": "2d2fda51-9f65-4ffe-a884-a79582de4b86", "urgency": true}
-{"input": "text: FBR-style content with CNIC, credential, urgency, payment signals", "input_category": "fbr", "privacy": {"exception_text_stored": false, "identifiers_stored": false, "raw_image_stored": false, "raw_input_stored": false, "raw_model_output_stored": false}, "result": {"red_flag_count": 4, "reply_draft_policy": "suppressed", "reply_draft_returned": false, "risk_label": "Likely scam", "safe_next_step_count": 4}, "signals": {"account_threat": false, "challan": false, "cnic": true, "courier": false, "credentials": true, "link": false, "otp": false, "payment": true, "refund_or_prize": true}, "timestamp": "2026-06-07T06:37:30.001113+00:00", "trace_id": "57521c66-f3df-412c-8c2f-9638fac910c7", "urgency": true}
-{"input": "text: Bank-style content with OTP, urgency, account-threat signals", "input_category": "bank", "privacy": {"exception_text_stored": false, "identifiers_stored": false, "raw_image_stored": false, "raw_input_stored": false, "raw_model_output_stored": false}, "result": {"red_flag_count": 3, "reply_draft_policy": "suppressed", "reply_draft_returned": false, "risk_label": "Likely scam", "safe_next_step_count": 5}, "signals": {"account_threat": true, "challan": false, "cnic": false, "courier": false, "credentials": false, "link": false, "otp": true, "payment": false, "refund_or_prize": false}, "timestamp": "2026-06-07T06:37:30.001192+00:00", "trace_id": "fa84e753-4492-473f-b797-b94eee8724bc", "urgency": true}
-{"input": "image: Courier-style content with link, urgency, courier signals", "input_category": "courier", "privacy": {"exception_text_stored": false, "identifiers_stored": false, "raw_image_stored": false, "raw_input_stored": false, "raw_model_output_stored": false}, "result": {"red_flag_count": 4, "reply_draft_policy": "suppressed", "reply_draft_returned": false, "risk_label": "Likely scam", "safe_next_step_count": 4}, "signals": {"account_threat": false, "challan": false, "cnic": false, "courier": true, "credentials": false, "link": true, "otp": false, "payment": false, "refund_or_prize": false}, "timestamp": "2026-06-07T06:37:30.001301+00:00", "trace_id": "eaf5013c-adb7-4b06-9e4c-4e785e4c776a", "urgency": true}
-{"input": "image: Marketplace-style content with credential signals", "input_category": "marketplace", "privacy": {"exception_text_stored": false, "identifiers_stored": false, "raw_image_stored": false, "raw_input_stored": false, "raw_model_output_stored": false}, "result": {"red_flag_count": 3, "reply_draft_policy": "suppressed", "reply_draft_returned": false, "risk_label": "Likely scam", "safe_next_step_count": 4}, "signals": {"account_threat": false, "challan": false, "cnic": false, "courier": false, "credentials": true, "link": false, "otp": false, "payment": false, "refund_or_prize": false}, "timestamp": "2026-06-07T06:37:30.001851+00:00", "trace_id": "dd5000f9-d612-4ee8-a1ce-d9c41a8ef347", "urgency": false}
-{"input": "image: Traffic-challan-style content with link, urgency, payment, challan signals", "input_category": "traffic_challan", "privacy": {"exception_text_stored": false, "identifiers_stored": false, "raw_image_stored": false, "raw_input_stored": false, "raw_model_output_stored": false}, "result": {"red_flag_count": 3, "reply_draft_policy": "suppressed", "reply_draft_returned": false, "risk_label": "Likely scam", "safe_next_step_count": 4}, "signals": {"account_threat": false, "challan": true, "cnic": false, "courier": false, "credentials": false, "link": true, "otp": false, "payment": true, "refund_or_prize": false}, "timestamp": "2026-06-07T06:37:30.002064+00:00", "trace_id": "19c0628b-a2ff-45ff-a35e-2a1e6a9d9335", "urgency": true}
+{"exception_text_stored": false, "identifiers_stored": false, "input": "text: PAKISTAN POST: [NAME_OR_ENTITY] parcel [ADDRESS]. [NAME_OR_ENTITY] Rs. 85 today at [LINK] or the parcel will be destroyed.", "input_category": "courier", "input_storage": "redacted_text", "raw_image_stored": false, "raw_input_stored": false, "raw_model_output_stored": false, "red_flag_count": 4, "reply_draft_policy": "suppressed", "reply_draft_returned": false, "result_summary": "Likely scam: Courier-style content with link, urgency, payment, courier signals. Known courier pattern. Strong scam indicators were found; avoid payments, links, and sharing credentials.", "risk_label": "Likely scam", "safe_next_step_count": 4, "scam_tactics": "link, urgency, payment, courier", "signal_account_threat": false, "signal_challan": false, "signal_cnic": false, "signal_courier": true, "signal_credentials": false, "signal_link": true, "signal_otp": false, "signal_payment": true, "signal_refund_or_prize": false, "timestamp": "2026-06-07T06:49:33.145291+00:00", "trace_id": "57c92b49-6166-41d4-9f98-ded53729c963", "urgency": true}
+{"exception_text_stored": false, "identifiers_stored": false, "input": "text: FBR REFUND: [NAME_OR_ENTITY] are eligible for Rs 42,[NUMBER]. [NAME_OR_ENTITY] your CNIC and bank card [REDACTED] at the link today to receive payment.", "input_category": "fbr", "input_storage": "redacted_text", "raw_image_stored": false, "raw_input_stored": false, "raw_model_output_stored": false, "red_flag_count": 4, "reply_draft_policy": "suppressed", "reply_draft_returned": false, "result_summary": "Likely scam: FBR-style content with CNIC, credential, urgency, payment signals. Known FBR pattern. Strong scam indicators were found; avoid payments, links, and sharing credentials.", "risk_label": "Likely scam", "safe_next_step_count": 4, "scam_tactics": "cnic, credentials, urgency, payment, refund_or_prize", "signal_account_threat": false, "signal_challan": false, "signal_cnic": true, "signal_courier": false, "signal_credentials": true, "signal_link": false, "signal_otp": false, "signal_payment": true, "signal_refund_or_prize": true, "timestamp": "2026-06-07T06:49:33.145489+00:00", "trace_id": "18b01fe5-eed0-49c8-9457-a6c9f49ccf71", "urgency": true}
+{"exception_text_stored": false, "identifiers_stored": false, "input": "text: HBL [NAME_OR_ENTITY]: [NAME_OR_ENTITY] account [REDACTED] be suspended. [NAME_OR_ENTITY] the OTP [REDACTED] to your phone with our support team immediately.", "input_category": "bank", "input_storage": "redacted_text", "raw_image_stored": false, "raw_input_stored": false, "raw_model_output_stored": false, "red_flag_count": 3, "reply_draft_policy": "suppressed", "reply_draft_returned": false, "result_summary": "Likely scam: Bank-style content with OTP, urgency, account-threat signals. Known bank pattern. Strong scam indicators were found; avoid payments, links, and sharing credentials.", "risk_label": "Likely scam", "safe_next_step_count": 5, "scam_tactics": "otp, urgency, account_threat", "signal_account_threat": true, "signal_challan": false, "signal_cnic": false, "signal_courier": false, "signal_credentials": false, "signal_link": false, "signal_otp": true, "signal_payment": false, "signal_refund_or_prize": false, "timestamp": "2026-06-07T06:49:33.145659+00:00", "trace_id": "fc38b636-2907-4ace-890a-c70f2f5350bc", "urgency": true}
+{"exception_text_stored": false, "identifiers_stored": false, "input": "image: Courier-style content with link, urgency, courier signals", "input_category": "courier", "input_storage": "image_description_only", "raw_image_stored": false, "raw_input_stored": false, "raw_model_output_stored": false, "red_flag_count": 4, "reply_draft_policy": "suppressed", "reply_draft_returned": false, "result_summary": "Likely scam: Courier-style content with link, urgency, courier signals. Known courier pattern. Strong scam indicators were found; avoid payments, links, and sharing credentials.", "risk_label": "Likely scam", "safe_next_step_count": 4, "scam_tactics": "link, urgency, courier", "signal_account_threat": false, "signal_challan": false, "signal_cnic": false, "signal_courier": true, "signal_credentials": false, "signal_link": true, "signal_otp": false, "signal_payment": false, "signal_refund_or_prize": false, "timestamp": "2026-06-07T06:49:33.145799+00:00", "trace_id": "d83cb31c-44bb-4fc1-8d13-68776e509b33", "urgency": true}
+{"exception_text_stored": false, "identifiers_stored": false, "input": "image: Marketplace-style content with credential signals", "input_category": "marketplace", "input_storage": "image_description_only", "raw_image_stored": false, "raw_input_stored": false, "raw_model_output_stored": false, "red_flag_count": 3, "reply_draft_policy": "suppressed", "reply_draft_returned": false, "result_summary": "Likely scam: Marketplace-style content with credential signals. Known marketplace pattern. Strong scam indicators were found; avoid payments, links, and sharing credentials.", "risk_label": "Likely scam", "safe_next_step_count": 4, "scam_tactics": "credentials", "signal_account_threat": false, "signal_challan": false, "signal_cnic": false, "signal_courier": false, "signal_credentials": true, "signal_link": false, "signal_otp": false, "signal_payment": false, "signal_refund_or_prize": false, "timestamp": "2026-06-07T06:49:33.146161+00:00", "trace_id": "aed94a0c-db03-48fc-92f7-ec8ba14b9124", "urgency": false}
+{"exception_text_stored": false, "identifiers_stored": false, "input": "image: Traffic-challan-style content with link, urgency, payment, challan signals", "input_category": "traffic_challan", "input_storage": "image_description_only", "raw_image_stored": false, "raw_input_stored": false, "raw_model_output_stored": false, "red_flag_count": 3, "reply_draft_policy": "suppressed", "reply_draft_returned": false, "result_summary": "Likely scam: Traffic-challan-style content with link, urgency, payment, challan signals. Known traffic-challan pattern. Strong scam indicators were found; avoid payments, links, and sharing credentials.", "risk_label": "Likely scam", "safe_next_step_count": 4, "scam_tactics": "link, urgency, payment, challan", "signal_account_threat": false, "signal_challan": true, "signal_cnic": false, "signal_courier": false, "signal_credentials": false, "signal_link": true, "signal_otp": false, "signal_payment": true, "signal_refund_or_prize": false, "timestamp": "2026-06-07T06:49:33.146349+00:00", "trace_id": "bf534dfe-991e-4291-93d5-5994f0964c19", "urgency": true}

traces/dataset_card.md

@@ -34,13 +34,23 @@ and convert it into allow-listed categories, booleans, buckets, and counts.
 ## Fields
 
 - Trace identity: random trace ID and UTC timestamp
-- `input`: a fixed-template `text: ...` or `image: ...` description
+- `input`: aggressively redacted, length-limited text for text submissions, or
+  a fixed-template `image: ...` description
 - `input_category`: deterministic category such as courier, bank, or FBR
 - `urgency`: deterministic boolean urgency signal
-- Deterministic signals: OTP, CNIC, credentials, link, payment,
-  refund/prize, courier, challan, and account threat
-- Final risk label and output item counts
-- Explicit privacy flags
+- `result_summary`: deterministic summary of the mapped pattern, whether it is
+  known or unclassified, and why the risk label matters
+- `scam_tactics`: readable comma-separated tactics
+- Flat boolean signal columns such as `signal_link`, `signal_payment`, and
+  `signal_credentials`
+- Flat result columns such as `risk_label`, `red_flag_count`, and
+  `reply_draft_policy`
+- `input_storage`: `redacted_text` or `image_description_only`
+- Flat privacy flags confirming that raw input, images, identifiers, model
+  output, and exception text are not stored
+
+Every dataset cell is a scalar string, number, or boolean. No column contains a
+dictionary or nested object, which keeps the Hugging Face table easy to read.
 
 Records do not contain pipeline steps, cache fields, app commits, failure
 details, request source, schema versions, size buckets, language hints, or
@@ -50,7 +60,7 @@ Modal metadata.
 
 The dataset never stores:
 
-- Raw or redacted message text
+- Raw message text
 - Screenshots, image bytes, or base64
 - URLs, phone numbers, CNICs, names, addresses, account/card numbers, or
   tracking numbers
@@ -58,7 +68,13 @@ The dataset never stores:
   output
 - Exceptions, credentials, tokens, or endpoint headers
 
-Summaries use fixed templates. Regex detection happens transiently in memory.
+Text traces store an aggressively redacted form capped at 500 characters.
+Regexes remove common URLs, emails, phones, CNICs, card/account numbers,
+credentials, addresses, tracking IDs, long numbers, and title-case names or
+entities. Regex redaction cannot guarantee removal of every possible
+identifier, so users should opt out when submitting sensitive content.
+
+Images store only fixed descriptions; screenshots and OCR text are not stored.
 Users see a checked trace disclosure in the app and may opt out before each
 request.
 
@@ -66,13 +82,13 @@ request.
 
 Seed traces represent the six public examples bundled with Pakistan Notice
 Helper. Runtime traces may represent successful, rejected, or failed requests.
-A trace reports whether the existing Modal request occurred, but trace
-generation itself does not invoke the model.
+Trace generation itself does not invoke the model.
 
 ## Limitations
 
 - Regex signals and category detection are approximate.
-- Duration and input sizes are deliberately bucketed.
+- Regex redaction may miss unusual personal or confidential information.
+- Novelty is not researched against external threat-intelligence sources.
 - The dataset cannot reproduce original messages or screenshots.
 - A risk label is safety guidance, not official verification.
 

traces/runtime.py

@@ -55,6 +55,44 @@ EXAMPLE_PROFILES = {
     "image-mobile": ("image", "marketplace", {"credentials"}),
     "image-traffic": ("image", "traffic_challan", {"link", "urgency", "payment", "challan"}),
 }
+RESULT_GUIDANCE = {
+    "Looks normal": (
+        "No strong scam indicators were found, but verify through an official "
+        "channel."
+    ),
+    "Verify first": (
+        "Limited or ambiguous warning signs were found; verify independently."
+    ),
+    "Suspicious": (
+        "Multiple warning signs were found; use caution and verify independently."
+    ),
+    "Likely scam": (
+        "Strong scam indicators were found; avoid payments, links, and sharing "
+        "credentials."
+    ),
+    "Inappropriate": "The content was not suitable for a scam-risk assessment.",
+    "none": "No completed assessment result was available.",
+}
+CATEGORY_DISPLAY_NAMES = {
+    "fbr": "FBR",
+    "bank": "bank",
+    "wallet": "mobile-wallet",
+    "utility": "utility",
+    "traffic_challan": "traffic-challan",
+    "courier": "courier",
+    "customs": "customs",
+    "university": "education",
+    "job": "job",
+    "marketplace": "marketplace",
+    "unknown": "unclassified",
+}
+SENSITIVE_VALUE_PATTERN = re.compile(
+    r"\b(?:password|passcode|pin|otp|cvv|account(?: number)?|card(?: number)?|"
+    r"tracking(?: id| number)?|consignment(?: id| number)?|reference(?: id| number)?)"
+    r"\s*(?:is|:|#|-)?\s*[A-Za-z0-9@._/-]{2,}",
+    re.I,
+)
+TITLE_CASE_PATTERN = re.compile(r"\b[A-Z][a-z]{2,}\b")
 
 
 def detect_signals(text: str, example_id: str = "") -> dict[str, bool]:
@@ -127,6 +165,51 @@ def safe_description(category: str, signals: dict[str, bool]) -> str:
     return f"{category_labels[category]} content{suffix}"
 
 
+def redact_text(text: str) -> str:
+    value = re.sub(r"\s+", " ", text or "").strip()
+    value = SENSITIVE_VALUE_PATTERN.sub(
+        lambda match: match.group(0).split()[0] + " [REDACTED]",
+        value,
+    )
+    replacements = (
+        (r"https?://\S+|www\.\S+", "[LINK]"),
+        (r"\b[\w.+-]+@[\w.-]+\.[A-Za-z]{2,}\b", "[EMAIL]"),
+        (r"\b\d{5}-\d{7}-\d\b", "[CNIC]"),
+        (r"(?<!\d)(?:\+?92[- ]?|0)?3\d{2}[- ]?\d{7}(?!\d)", "[PHONE]"),
+        (r"\bPK\d{2}[A-Z0-9]{10,30}\b", "[ACCOUNT]"),
+        (r"(?<!\d)(?:\d[ -]?){12,19}(?!\d)", "[CARD_NUMBER]"),
+        (r"\b\d{3,}\b", "[NUMBER]"),
+        (
+            r"\b(?:address|location|house|street|road|flat)\b"
+            r"[^,.;]{0,60}",
+            "[ADDRESS]",
+        ),
+    )
+    for pattern, replacement in replacements:
+        value = re.sub(pattern, replacement, value, flags=re.I)
+    value = TITLE_CASE_PATTERN.sub("[NAME_OR_ENTITY]", value)
+    value = re.sub(
+        r"(?:\[[A-Z_]+\]\s*){2,}",
+        lambda match: match.group(0).strip() + " ",
+        value,
+    )
+    return value[:500] or "[EMPTY]"
+
+
+def result_summary(
+    risk_label: str,
+    category: str,
+    signals: dict[str, bool],
+) -> str:
+    pattern = safe_description(category, signals)
+    novelty = (
+        "Unclassified pattern; this does not confirm a new scam type."
+        if category == "unknown"
+        else f"Known {CATEGORY_DISPLAY_NAMES[category]} pattern."
+    )
+    return f"{risk_label}: {pattern}. {novelty} {RESULT_GUIDANCE[risk_label]}"
+
+
 def build_input_profile(text: str, image_data_url: str, example_id: str = "") -> dict[str, Any]:
     profile = EXAMPLE_PROFILES.get(example_id)
     if profile:
@@ -137,12 +220,18 @@ def build_input_profile(text: str, image_data_url: str, example_id: str = "") ->
         input_type = "text"
     signals = detect_signals(text, example_id)
     category = detect_category(text, signals, example_id)
+    tactics = [name for name, enabled in signals.items() if enabled]
     return {
-        "input": f"{input_type}: {safe_description(category, signals)}",
+        "input": (
+            f"text: {redact_text(text)}"
+            if input_type == "text"
+            else f"image: {safe_description(category, signals)}"
+        ),
         "input_category": category,
         "urgency": signals["urgency"],
-        "signals": {
-            name: enabled
+        "scam_tactics": ", ".join(tactics) if tactics else "none",
+        **{
+            f"signal_{name}": enabled
             for name, enabled in signals.items()
             if name != "urgency"
         },
@@ -161,33 +250,38 @@ def build_trace_record(
     if risk_label not in RISK_LABELS:
         risk_label = "none"
     input_profile = build_input_profile(text, image_data_url, example_id)
+    signals = detect_signals(text, example_id)
+    category = input_profile["input_category"]
+    assessment = assessment or {}
     return {
         "trace_id": trace_id,
         "timestamp": datetime.now(timezone.utc).isoformat(),
         **input_profile,
-        "result": {
-            "risk_label": risk_label,
-            "red_flag_count": min(len((assessment or {}).get("red_flags", [])), 50),
-            "safe_next_step_count": min(
-                len((assessment or {}).get("safe_next_steps", [])),
-                50,
-            ),
-            "reply_draft_returned": bool((assessment or {}).get("reply_draft")),
-            "reply_draft_policy": (
-                "allowed"
-                if risk_label in {"Verify first", "Suspicious"}
-                else "suppressed"
-                if risk_label != "none"
-                else "not_applicable"
-            ),
-        },
-        "privacy": {
-            "raw_input_stored": False,
-            "raw_image_stored": False,
-            "raw_model_output_stored": False,
-            "exception_text_stored": False,
-            "identifiers_stored": False,
-        },
+        "result_summary": result_summary(risk_label, category, signals),
+        "risk_label": risk_label,
+        "red_flag_count": min(len(assessment.get("red_flags", [])), 50),
+        "safe_next_step_count": min(
+            len(assessment.get("safe_next_steps", [])),
+            50,
+        ),
+        "reply_draft_returned": bool(assessment.get("reply_draft")),
+        "reply_draft_policy": (
+            "allowed"
+            if risk_label in {"Verify first", "Suspicious"}
+            else "suppressed"
+            if risk_label != "none"
+            else "not_applicable"
+        ),
+        "input_storage": (
+            "redacted_text"
+            if input_profile["input"].startswith("text: ")
+            else "image_description_only"
+        ),
+        "raw_input_stored": False,
+        "raw_image_stored": False,
+        "raw_model_output_stored": False,
+        "exception_text_stored": False,
+        "identifiers_stored": False,
     }
 
 
@@ -201,9 +295,28 @@ def validate_trace(record: Any) -> list[str]:
         "input",
         "input_category",
         "urgency",
-        "signals",
-        "result",
-        "privacy",
+        "scam_tactics",
+        "signal_otp",
+        "signal_cnic",
+        "signal_credentials",
+        "signal_link",
+        "signal_payment",
+        "signal_refund_or_prize",
+        "signal_courier",
+        "signal_challan",
+        "signal_account_threat",
+        "result_summary",
+        "risk_label",
+        "red_flag_count",
+        "safe_next_step_count",
+        "reply_draft_returned",
+        "reply_draft_policy",
+        "input_storage",
+        "raw_input_stored",
+        "raw_image_stored",
+        "raw_model_output_stored",
+        "exception_text_stored",
+        "identifiers_stored",
     }
     missing = required - record.keys()
     if missing:
@@ -221,11 +334,28 @@ def validate_trace(record: Any) -> list[str]:
         errors.append("Input category must be a string.")
     if not isinstance(record.get("urgency"), bool):
         errors.append("Urgency must be boolean.")
-    if record.get("result", {}).get("risk_label") not in RISK_LABELS:
+    if not isinstance(record.get("result_summary"), str):
+        errors.append("Result summary must be a string.")
+    if record.get("risk_label") not in RISK_LABELS:
         errors.append("Invalid risk label.")
-    privacy = record.get("privacy", {})
-    if not isinstance(privacy, dict) or any(privacy.get(key) is not False for key in privacy):
-        errors.append("Privacy flags must all be false.")
+    private_storage_flags = (
+        "raw_input_stored",
+        "raw_image_stored",
+        "raw_model_output_stored",
+        "exception_text_stored",
+        "identifiers_stored",
+    )
+    if any(
+        record.get(key) is not False for key in private_storage_flags
+    ):
+        errors.append("Raw/private storage flags must all be false.")
+    if record.get("input_storage") not in {
+        "redacted_text",
+        "image_description_only",
+    }:
+        errors.append("Invalid input storage category.")
+    if any(isinstance(value, (dict, list)) for value in record.values()):
+        errors.append("Trace columns must contain scalar values only.")
     forbidden_keys = {
         "schema_version",
         "app_commit",