pip install nightfall=1.2.0
pip install requests

import requests
import json
import os
import csv
from nightfall import Nightfall

nightfall_api_key = os.environ.get('NIGHTFALL_API_KEY')
airtable_api_key = os.environ.get('AIRTABLE_API_KEY')

detectionRuleUUID = os.environ.get('DETECTION_RULE_UUID')

nightfall = Nightfall(nightfall_api_key)

[
    {
        "base_id": "appp4vxoDwgURFwYp",
        "base_name": "Product Planning",
        "tables": [
            "Stories", 
            "Epics", 
            "Sprints", 
            "Release Milestones", 
            "Facets", 
            "App Sections"
        ]
    },
    {
        "base_id": "appwWnUfLVJhltYQv",
        "base_name": "Product Launch",
        "tables": [
            "Features",
            "Product Themes",
            "Monthly Newsletters"
        ]
    }
  ]

airtable_config = json.load(open('config.json', 'r'))
airtable_base_url = 'https://api.airtable.com/v0'
airtable_headers = {
  "Authorization": f"Bearer {airtable_api_key}"
}

all_airtable = []

for base in airtable_config:
    base_id = base['base_id']
    req_tables = [i.replace(' ', '%20') for i in base['tables']]

    for table in req_tables:
        airtable_url = f"{airtable_base_url}/{base_id}/{table}"
        airtable_response = requests.get(airtable_url, headers=airtable_headers)
        airtable_content = json.loads(airtable_response.text)

        for i in airtable_content['records']:
            # We enrich each datum with metadata so it can be easily located later
            cur_str = f"BaseName: {base['base_name']} -|- BaseID: {base_id} -|- Table: {table} -|- Record: {i['id']} -|- Field: "

            for j in i['fields']:
                str_to_send = f"{cur_str}{j} -|- Content: {i['fields'][j]}"
                all_airtable.append(str_to_send)

def str_parser(sent_str):
    split_str = sent_str.split(' -|- ')
    split_dict = {i[:i.find(': ')]: i[i.find(': ')+2:] for i in split_str[:5]}
    findertext = f" -|- Field: {split_dict['Field']} -|- Content: "
    split_dict['Content'] = sent_str[sent_str.find(findertext)+len(findertext):]
    return split_dict

all_findings = []
all_findings.append(
  [
    'base_name', 'base_id', 'table_name', 'record_id', 'field',
    'detector', 'confidence', 
    'finding_start', 'finding_end', 'finding'
  ]
)

findings, redactions = nightfall.scan_text(
    all_airtable,
    detection_rule_uuids=[detectionRuleUUID]
)

# This level of loop corresponds to each list item sent to the Nightfall API
for field_idx, field_findings in enumerate(findings):
    
    sent_str = all_airtable[field_idx]
    # We call the helper function we defined earlier to help us parse the string sent to the Nightfall API
    parsed_str = str_parser(sent_str)
    offset = len(sent_str) - len(parsed_str['Content'])

    # This loop corresponds to each finding within an item sent to the Nightfall API
    for finding in field_findings:

        # If a finding is returned within the metadata for the content, we discount it
        if finding.byte_range.start < offset:
            continue

        # Add finding data to all_findings
        all_findings.append([
            parsed_str['BaseName'],
            parsed_str['BaseID'],
            parsed_str['Table'],
            parsed_str['Record'],
            parsed_str['Field'],
            finding.detector_name,
            finding.confidence.value,
            finding.byte_range.start,
            finding.byte_range.end,
            finding.finding
        ])

if len(all_findings) > 1:
    with open('output_file.csv', 'w') as output_file:
        csv_writer = csv.writer(output_file, delimiter = ',')
        csv_writer.writerows(all_findings)
else:
    print('No sensitive data detected. Hooray!')

airtable_config = json.load(open('config.json', 'r'))
airtable_base_url = 'https://api.airtable.com/v0'
airtable_headers = {
  "Authorization": f"Bearer {airtable_api_key}"
}

all_airtable = []
all_airtable.append(
  ['base_name', 'base_id', 'table_name', 'record_id', 'field', 'content']
)

filename = "nf_airtable_input-" + str(int(time.time())) + ".csv"

for base in airtable_config:
    base_id = base['base_id']
    req_tables = [i.replace(' ', '%20') for i in base['tables']]

    for table in req_tables:
        airtable_url = f"{airtable_base_url}/{base_id}/{table}"
        airtable_response = requests.get(airtable_url, headers=airtable_headers)
        airtable_content = json.loads(airtable_response.text)

        for i in airtable_content['records']:
            for j in i['fields']:
                # We enrich each datum with metadata so it can be easily located later
                # BaseName, BaseID, Table, Record, Field, Content
                row = [base['base_name'], base_id, table, i['id'], j, i['fields'][j]]
                all_airtable.append(row)

with open(filename, 'w') as output_file:
    csv_writer = csv.writer(output_file, delimiter=',')
    csv_writer.writerows(all_airtable)

print("Airtable Data Written to: ", filename)

scan_id, message = nightfall.scan_text(
    filename,
    webhook_url=WEBHOOK_URL,
    detection_rule_uuids=[detectionRuleUUID],
)

pip install nightfall=0.6.0

import requests
import psycopg2
import os
import sys
import json
from nightfall import Nightfall

size_limit = 500000
length_limit = 50000

nightfall = Nightfall(os.environ['NIGHTFALL_API_KEY'])

detectionRuleUUID = os.environ.get('DETECTION_RULE_UUID')

connection = psycopg2.connect(
        host = os.environ.get('RDS_ENDPOINT'),
        port = 5432,
        user = os.environ.get('RDS_USER'),
        password = os.environ.get('RDS_PASSWORD'),
        database = os.environ.get('RDS_DATABASE')
    )
table_name = os.environ.get('RDS_TABLE')
primary_key = os.environ.get('RDS_PRIMARYKEY')
cursor = connection.cursor()

sql = f"""
SELECT *
FROM {table_name}
"""

cursor.execute(sql)
connection.commit()

cols = [i.name for i in cursor.description]
data = cursor.fetchall()

primary_key_col = []

if len(data) == 0:
  raise Exception('Table is empty! No data to scan.')

all_findings = []
for col_idx, col in enumerate(columns):
    payload = [str(i[col_idx]) for i in data]
    if col == primary_key:
      primary_key_col = payload
      col_size = sys.getsizeof(payload)

    if col_size < size_limit:
   	 resp = nightfall.scanText(
        [payload],
        detection_rule_uuids=[detectionRuleUUID])
    
     col_resp = json.loads(resp)
      
for item_idx, item in enumerate(col_resp):
  if item != None:
    for finding in item:
      finding['column'] = col
      try:
        finding['index'] = primary_key_col[item_idx]
      except:
          finding['index'] = item_idx
      all_findings.append(finding)

col_resp = []
chunks = []
chunk = []
running_size = 0
big_items = []

for item_idx, item in enumerate(payload):
  item_size = sys.getsizeof(item)
  if (running_size + item_size < size_limit) and (len(chunk) < length_limit):
    chunk.append(item)
    running_size += item_size
  elif item_size < size_limit:
    chunks.append(chunk)
    chunk = [item]
    running_size = item_size
  else:
    if len(chunk) < length_limit:
      chunk.append('')
    else:
      chunks.append(chunk)
      chunk = ['']
      big_items.append(item_idx)
      chunks.append(chunk)

chunk_cursor = 0

for chunk in chunks:
  resp = nightfall.scanText(
        [chunk],
        detection_rule_uuids=[conditionSetUUID])
  col_resp.extend(json.loads(resp.text))
  chunk_cursor += len(chunk)

for item_idx, item in enumerate(col_resp):
  if item != None:
    for finding in item:
      finding['column'] = col
      try:
        finding['index'] = primary_key_col[item_idx]
      except:
          finding['index'] = item_idx
      all_findings.append(finding)

for big in big_items:
  item_size = sys.getsizeof(big)
    chunks_req = (item_size // size_limit) + 1
    chunk_len = len(item) // chunks_req
    cursor = 0
    item_findings = []
    for _ in range(chunks_req):
        p = item[cursor : min(cursor + chunk_len, len(item))]
        resp = nightfall.scanText({
        [[p]],
        detection_rule_uuids=[conditionSetUUID])
        item_findings.extend(json.loads(resp.text))
        cursor += chunk_len
  
  if item_findings == []:
    raise Exception(f"Error while scanning large item at column {col}, Index {primary_key_col[big]}")
  for find_chunk in item_resp:
      if find_chunk != None:
        for finding in find_chunk:
          finding['column'] = col
          try:
            finding['index'] = primary_key_col[big]
          except:
            finding['index'] = big
          all_findings.append(finding)

printf("{len(all_findings)} sensitive findings in {os.environ.get('RDS_TABLE')}")
with open('rds_findings.json', 'w') as output_file:
  json.dump(all_findings, output_file)

import requests
import psycopg2
import os
import sys
import json
from nightfall.api import Nightfall

size_limit = 500000
length_limit = 50000

nightfall = Nightfall(os.environ['NIGHTFALL_API_KEY'])

detectionRuleUUID = os.environ.get('DETECTION_RULE_UUID')

def get_from_rds():
    '''
    Gets data to be scanned from postgres table in RDS.
    Inputs: 
        None (all required info should be stored as environment variables)
    Returns: 
        data, list[tuple()] - data from postgres table as a list of tuples
        cols, list[str] - list of column names
        primary_key, str - name of primary key column
    '''
    connection = psycopg2.connect(
        host = os.environ.get('RDS_ENDPOINT'),
        port = 5432,
        user = os.environ.get('RDS_USER'),
        password = os.environ.get('RDS_PASSWORD'),
        database = os.environ.get('RDS_DATABASE')
    )
    table_name = os.environ.get('RDS_TABLE')
    primary_key = os.environ.get('RDS_PRIMARYKEY')
    cursor = connection.cursor()

    sql = f"""
    SELECT *
    FROM {table_name}
    """

    cursor.execute(sql)
    connection.commit()

    cols = [i.name for i in cursor.description]
    data = cursor.fetchall()

    return data, cols, primary_key

def nightfall_scan(payload):
    '''
    Calls the Nightfall scan API on input text
    Inputs:
        payload, list[str] - list of strings to be scanned
    Returns:
        resp - http response from the Nightfall API containing scan results
    '''
    return nightfall.scanText(
        [payload],
        detection_rule_uuids=[detectionRuleUUID])

def craft_chunks(payload, size_limit, length_limit):
    '''
    Chunks a payload into smaller bits processable by the Nightfall scan API
    Inputs:
        payload, list[str] - list of strings to be scanned
        size_limit, int - maximum data size allowed by Nightfall API per request
        length_limit, int - maximum no. of items allowed by Nightfall API per request
    Returns:
        chunks, list[list[str]] - list of lists of strings to be scanned
        big_items, list[int] - list of indices of items that exceed the size_limit on their own
    '''
    chunks = []
    chunk = []
    running_size = 0
    big_items = []
    for item_idx, item in enumerate(payload):
        item_size = sys.getsizeof(item)
        if (running_size + item_size < size_limit) and (len(chunk) < length_limit):
            chunk.append(item)
            running_size += item_size
        elif item_size < size_limit:
            chunks.append(chunk)
            chunk = [item]
            running_size = item_size
        else:
            if len(chunk) < length_limit:
                chunk.append('')
            else:
                chunks.append(chunk)
                chunk = ['']
            big_items.append(item_idx)
    chunks.append(chunk)
    return chunks, big_items

def scan_big_item(item, size_limit):
    '''
    Chunks a single large block of text and sends it to the Nightfall API
    in processable bits
    Inputs:
        item, str - a single string to be scanned by the Nightfall API
        size_limit, int - maximum data size allowed by Nightfall API per request
    Returns:
        item_findings, list[list[dict]] - findings from the Nightfall API for the entire item
    '''
    item_size = sys.getsizeof(item)
    chunks_req = (item_size // size_limit) + 1
    chunk_len = len(item) // chunks_req
    cursor = 0
    item_findings = []
    for _ in range(chunks_req):
        p = item[cursor : min(cursor + chunk_len, len(item))]
        resp = nightfall.scanText(
          [[p]],
          detection_rule_uuids=[detectionRuleUUID])
        item_findings.extend(json.loads(resp.text))
        cursor += chunk_len
    return item_findings

if __name__ == '__main__':
    # This script will be for Postgres but other SQL varieties 
    # will work with modifications
    data, columns, primary_key = get_from_rds()
    primary_key_col = []
    if len(data) == 0:
        raise Exception('Table is empty! No data to scan.')

    all_findings = []
    for col_idx, col in enumerate(columns):
        payload = [str(i[col_idx]) for i in data]
        if col == primary_key:
            primary_key_col = payload
        col_size = sys.getsizeof(payload)

        if col_size < size_limit:
            resp = resp = nightfall.scanText({
              [[p]],
              detection_rule_uuids=[detectionRuleUUID])
            col_resp = json.loads(resp)
        else:
            col_resp = []
            chunks, big_items = craft_chunks(payload, size_limit, length_limit)
            chunk_cursor = 0
            for chunk in chunks:
                resp = nightfall.scanText(
                  [chunk],
                  detection_rule_uuids=[detectionRuleUUID])
                col_resp.extend(json.loads(resp))
                chunk_cursor += len(chunk)
            
            for big in big_items:
                item_resp = scan_big_item(payload[big], size_limit)
                if item_resp == None:
                    raise Exception(f"Error while scanning large item at column {col}, Index {primary_key_col[big]}")
                for find_chunk in item_resp:
                    if find_chunk != None:
                        for finding in find_chunk:
                            finding['column'] = col
                            try:
                                finding['index'] = primary_key_col[big]
                            except:
                                finding['index'] = big
                            all_findings.append(finding)

        # Add location within source Table
        for item_idx, item in enumerate(col_resp):
            if item != None:
                for finding in item:
                    finding['column'] = col
                    try:
                        finding['index'] = primary_key_col[item_idx]
                    except:
                        finding['index'] = item_idx
                    all_findings.append(finding)

    print(f"{len(all_findings)} sensitive findings in {os.environ.get('RDS_TABLE')}")
    with open('rds_findings.json', 'w') as output_file:
        json.dump(all_findings, output_file)

connection = psycopg2.connect(
        host = os.environ.get('RDS_ENDPOINT'),
        port = 5432,
        user = os.environ.get('RDS_USER'),
        password = os.environ.get('RDS_PASSWORD'),
        database = os.environ.get('RDS_DATABASE')
    )
table_name = os.environ.get('RDS_TABLE')
primary_key = os.environ.get('RDS_PRIMARYKEY')
cursor = connection.cursor()

sql = f"""
SELECT *
FROM {table_name}
"""

cursor.execute(sql)
connection.commit()

cols = [i.name for i in cursor.description]
data = cursor.fetchall()

primary_key_col = []

if len(data) == 0:
  raise Exception('Table is empty! No data to scan.')

filename = "nf_rds_input-" + str(int(time.time())) + ".csv"  

for col_idx, col in enumerate(columns):
    payload = [str(i[col_idx]) for i in data]   
    with open(filename, 'w') as output_file:
      csv_writer = csv.writer(output_file, delimiter=',')
      csv_writer.writerows(payload)
     
print("RDS Data Written to: ", filename)

docker pull sebp/elk

docker run -p 5601:5601 -p 9200:9200 -p 5044:5044 -it --name elk sebp/elk

pip install nightfall=1.2.0
pip install requests

import requests
import json
import os
import csv
from nightfall import Nightfall

elasticsearch_base_url = 'http://localhost:9200/kibana_sample_data_logs/_search'

nightfall_api_key = os.environ.get('NIGHTFALL_API_KEY')

detectionRuleUUID = os.environ.get('DETECTION_RULE_UUID')

nightfall = Nightfall(nightfall_api_key)

elasticsearch_payload = {"query": {"range": {"timestamp": {"gte" : "now-1h"}}}}

elasticsearch_headers = {
  'Content-Type': 'application/json'
}

elasticsearch_response = requests.get(
  url = elasticsearch_base_url,
  headers = elasticsearch_headers,
  params = elasticsearch_payload
)

logs = elasticsearch_response.json()['hits']['hits']

findings, redactions = nightfall.scan_text(
    [json.dumps(l) for l in logs],
    detection_rule_uuids=[detectionRuleUUID]
)

all_findings = []
all_findings.append(
    [
        'index_name', 'log_id', 'detector', 'confidence', 
        'finding_start', 'finding_end', 'finding'
    ]
)

# Each top level item in findings corresponds to one log
for log_idx, log_findings in enumerate(findings):
    for finding_idx, finding in enumerate(log_findings):

        index_name = logs[log_idx]['_index']
        log_id = logs[log_idx]['_id']

        row = [
            index_name,
            log_id,
            finding.detector_name,
            finding.confidence.value,
            finding.byte_range.start,
            finding.byte_range.end,
            finding.finding,
        ]
        all_findings.append(row)

if len(all_findings) > 1:
    with open('output_file.csv', 'w') as output_file:
        csv_writer = csv.writer(output_file, delimiter = ',')
        csv_writer.writerows(all_findings)
else:
    print('No sensitive data detected. Hooray!')

elasticsearch_base_url = 'http://localhost:9200/kibana_sample_data_logs/_search'

elasticsearch_payload = {"query": {"range": {"timestamp": {"gte" : "now-1h"}}}}

elasticsearch_headers = {
    'Content-Type': 'application/json'
}

elasticsearch_response = requests.get(
    url = elasticsearch_base_url,
    headers = elasticsearch_headers,
    params = elasticsearch_payload
)

logs = elasticsearch_response.json()['hits']['hits']

filename = "nf_elasticsearch_input-" + str(int(time.time())) + ".csv"  

with open(filename, 'w') as output_file:
    csv_writer = csv.writer(output_file, delimiter=',')
    csv_writer.writerows([json.dumps(l) for l in logs])
     
print("Elasticsearch Data Written to: ", filename)

pip install snowflake-connector-python
pip install nightfall=0.6.0

import requests
import snowflake.connector
import os
import sys
import json
from nightfall import Nightfall

size_limit = 500000
length_limit = 50000

nightfall = Nightfall(os.environ['NIGHTFALL_API_KEY'])

detectionRuleUUID = os.environ.get('DETECTION_RULE_UUID')

connection = snowflake.connector.connect(
  user=os.environ.get('SNOWFLAKE_USER'),
  password=os.environ.get('SNOWFLAKE_PASSWORD'),
  account=os.environ.get('SNOWFLAKE_ACCOUNT'),
  schema=os.environ.get('SNOWFLAKE_SCHEMA'),
  database=os.environ.get('SNOWFLAKE_DATABASE')
)
table_name = os.environ.get('SNOWFLAKE_TABLE')
primary_key = os.environ.get('SNOWFLAKE_PRIMARY_KEY')

cursor = connection.cursor()

sql = f"""
        SELECT *
        FROM {table_name}
        LIMIT 1000;
        """

cursor.execute(sql)

cols = [i[0] for i in cursor.description]
data = cursor.fetchall()

primary_key_col = []

if len(data) == 0:
  raise Exception('Table is empty! No data to scan.')

all_findings = []
for col_idx, col in enumerate(columns):
    payload = [str(i[col_idx]) for i in data]
    if col == primary_key:
      primary_key_col = payload
      col_size = sys.getsizeof(payload)

    if col_size < size_limit:
   	 resp = nightfall.scanText(
        [payload],
        detection_rule_uuids=[detectionRuleUUID])
    
     col_resp = json.loads(resp)

for item_idx, item in enumerate(col_resp):
  if item != None:
    for finding in item:
      finding['column'] = col
      try:
        finding['index'] = primary_key_col[item_idx]
      except:
          finding['index'] = item_idx
      all_findings.append(finding)

col_resp = []
chunks = []
chunk = []
running_size = 0
big_items = []

for item_idx, item in enumerate(payload):
  item_size = sys.getsizeof(item)
  if (running_size + item_size < size_limit) and (len(chunk) < length_limit):
    chunk.append(item)
    running_size += item_size
  elif item_size < size_limit:
    chunks.append(chunk)
    chunk = [item]
    running_size = item_size
  else:
    if len(chunk) < length_limit:
      chunk.append('')
    else:
      chunks.append(chunk)
      chunk = ['']
      big_items.append(item_idx)
      chunks.append(chunk)

chunk_cursor = 0

for chunk in chunks:
  resp = nightfall.scanText({
        "text": [chunk],
        "detectionRuleUUIDs": [conditionSetUUID]})
  col_resp.extend(json.loads(resp.text))
  chunk_cursor += len(chunk)
  
for item_idx, item in enumerate(col_resp):
  if item != None:
    for finding in item:
      finding['column'] = col
      try:
        finding['index'] = primary_key_col[item_idx]
      except:
          finding['index'] = item_idx
      all_findings.append(finding)

for big in big_items:
  item_size = sys.getsizeof(big)
    chunks_req = (item_size // size_limit) + 1
    chunk_len = len(item) // chunks_req
    cursor = 0
    item_findings = []
    for _ in range(chunks_req):
        p = item[cursor : min(cursor + chunk_len, len(item))]
        resp = nightfall.scanText({
        "text": [[p]],
        "detectionRuleUUIDs": [conditionSetUUID]})
        item_findings.extend(json.loads(resp.text))
        cursor += chunk_len
  
  if item_findings == []:
    raise Exception(f"Error while scanning large item at column {col}, Index {primary_key_col[big]}")
  for find_chunk in item_resp:
      if find_chunk != None:
        for finding in find_chunk:
          finding['column'] = col
          try:
            finding['index'] = primary_key_col[big]
          except:
            finding['index'] = big
          all_findings.append(finding)

print(f"{len(all_findings)} sensitive findings in {os.environ.get('SNOWFLAKE_TABLE')}")
with open('snowflake_findings.json', 'w') as output_file:
  json.dump(all_findings, output_file)

connection = snowflake.connector.connect(
  user=os.environ.get('SNOWFLAKE_USER'),
  password=os.environ.get('SNOWFLAKE_PASSWORD'),
  account=os.environ.get('SNOWFLAKE_ACCOUNT'),
  schema=os.environ.get('SNOWFLAKE_SCHEMA'),
  database=os.environ.get('SNOWFLAKE_DATABASE')
)
table_name = os.environ.get('SNOWFLAKE_TABLE')
primary_key = os.environ.get('SNOWFLAKE_PRIMARY_KEY')

cursor = connection.cursor()

sql = f"""
        SELECT *
        FROM {table_name}
        LIMIT 1000;
        """

cursor.execute(sql)

cols = [i[0] for i in cursor.description]
data = cursor.fetchall()

primary_key_col = []

if len(data) == 0:
  raise Exception('Table is empty! No data to scan.')

filename = "nf_snowflake_input-" + str(int(time.time())) + ".csv"  

for col_idx, col in enumerate(columns):
    payload = [str(i[col_idx]) for i in data]   
    with open(filename, 'w') as output_file:
      csv_writer = csv.writer(output_file, delimiter=',')
      csv_writer.writerows(payload)
     
print("Snowflake Data Written to: ", filename)

pip install boto3
pip install nightfall=1.2.0

import boto3
import requests
import json
import csv
import os
from nightfall import Nightfall

aws_session_token = os.environ.get('AWS_SESSION_TOKEN')
aws_access_key_id = os.environ.get('AWS_ACCESS_KEY_ID')
aws_secret_access_key = os.environ.get('AWS_SECRET_ACCESS_KEY')

nightfall_api_key = os.environ.get('NIGHTFALL_API_KEY')

detectionRuleUUID = os.environ.get('DETECTION_RULE_UUID')

nightfall = Nightfall(os.environ['NIGHTFALL_API_KEY'])

objects_to_scan = []
size_limit = 475000

all_findings = []
all_findings.append(
  [
    'bucket', 'object', 'detector', 'confidence', 
    'finding_byte_start', 'finding_byte_end',
    'finding_codepoint_start', 'finding_codepoint_end', 'fragment'
  ]
)

my_session = boto3.session.Session(
  aws_session_token = aws_session_token,
  aws_access_key_id = aws_access_key_id,
  aws_secret_access_key = aws_secret_access_key
)

s3 = my_session.resource('s3')

for bucket in s3.buckets.all():
  for obj in bucket.objects.all():
    temp_object = obj.get()
    size = temp_object['ContentLength']

    if size < size_limit:
      objects_to_scan.append((obj, temp_object['Body'].read().decode()))

for obj, data in objects_to_scan:
    findings, redactions = nightfall.scan_text(
        [data],
        detection_rule_uuids=[detectionRuleUUID]
    )

    for finding in findings[0]:
        row = [
            obj.bucket_name,
            obj.key,
            finding.detector_name,
            finding.confidence.value,
            finding.byte_range.start,
            finding.byte_range.end,
            finding.codepoint_range.start,
            finding.codepoint_range.end,
            finding.finding,
        ]
        all_findings.append(row)

if len(all_findings) > 1:
    with open('output_file.csv', 'w') as output_file:
        csv_writer = csv.writer(output_file, delimiter = ',')
        csv_writer.writerows(all_findings)
else:
      print('No sensitive data detected. Hooray!')

my_session = boto3.session.Session(
  aws_session_token = aws_session_token,
  aws_access_key_id = aws_access_key_id,
  aws_secret_access_key = aws_secret_access_key
)

s3 = my_session.resource('s3')

for bucket in s3.buckets.all():
  for obj in b.objects.all():
    # here we can call the file-scanning endpoints

1/part-00000-c5a881bb-58ff-4ee6-1111-b41ecff340a3-c000.gz.parquet
2/part-00000-d7a881cc-88cc-5ab7-2222-c41ecab340a4-c000.gz.parquet
3/part-00000-f5a991ab-59aa-7fa6-3333-d41eccd340a7-c000.gz.parquet

#Start the upload
curl --location --request POST 'https://api.nightfall.ai/v3/upload' \
--header 'Content-Type: application/json' \
--header 'Authorization: Bearer NF-<Your API Key>' \
--data-raw '{
    "fileSizeBytes": <Your File Size>,
    "mimeType" : "application/zip"
}'

#Resulting payload
{"id":"<Your File Upload ID>","fileSizeBytes":3693,"chunkSize":10485760,"mimeType":"application/zip"}

#Post the file using the 'id' from the returned payload in your path
curl --location --request PATCH 'https://api.nightfall.ai/v3/upload/<Your File Upload ID>' \
--header 'X-Upload-Offset: 0' \
--header 'Content-Type: application/octet-stream' \
--header 'Authorization: Bearer NF-<Your API Key>' \
--data-binary '@///Users/myuser/yourfilepath/userdata1.parquet'

#Finish the upload

curl --location --request POST 'https://api.nightfall.ai/v3/upload/<Your File Upload ID>/finish' \
--header 'Content-Type: application/json' \
--header 'Authorization: Bearer  NF-<Your API Key>'

# Scan the file using an alert config

curl --request POST \
     --url https://api.nightfall.ai/v3/upload/<Your File Upload ID>/scan \
     --header 'Accept: application/json' \
     --header 'Authorization: Bearer NF-<Your API Key>' \
     --header 'Content-Type: application/json' \
     --data '
{
     "policy": {
          "detectionRuleUUIDs": [
               "<Your detection Rule UUID>"
          ],
          "alertConfig": {
               "email": {
                    "address": "<Your Email Address>"
               }
          }
     },
     "requestMetadata": "scan of parquet file"
}
'

{
   "errors":null,
   "findingsPresent":true,
   "findingsURL":"https://files.nightfall.ai/18b8b6b8-59c9-4891-9f92-9c357acc19cd.json?Expires=1655306887&Signature=ASDFcCfQpEush3QrWmdZX9A9RePQjNHZTfRkBlgPwdPf~RcNnPYgYzt3G4AAzkI8IDbUdc4CzBbAROTx0oYOtNODCTdoKHKB7Q0a7~hzRNx3BHYHH1msdhkS1qTl3z82RCh6DZi~nk~Oa~yt-XZvAf3ui4MyNU0wyfqjbKO9o79Ec9YWqMdUmTOP1Ss39YmA71e6ky0VOdjdN4baoQV5VElTQ1rHrkdgYHz-95Dnzd3YK3IxGQR92AU7KA3X-rrcmpIJwMUIJSsl8~or0WIg5ar4U9Ood1BFSE~GmlQsKclEo1LEaX2KclWaQtjmN9~3IQnxOmkhPeAhEt-5n~Hbug__&Key-Pair-Id=ASDFOPZ1EKX0YC",
   "requestMetadata":"scan of go sdk for URLs sdk",
   "uploadID":"18b8b6b8-59c9-4891-9f92-9c357acc19cd",
   "validUntil":"2022-06-15T15:28:07.86163221Z"
}

DROP TABLE IF EXISTS `myTable`;

CREATE TABLE `myTable` (
  `id` mediumint(8) unsigned NOT NULL auto_increment,
  `name` varchar(255) default NULL,
  `phone` varchar(100) default NULL,
  `email` varchar(255) default NULL,
  `address` varchar(255) default NULL,
  `postalZip` varchar(10) default NULL,
  `region` varchar(50) default NULL,
  `country` varchar(100) default NULL,
  `alphanumeric` varchar(255),
  `text` TEXT default NULL,
  PRIMARY KEY (`id`)
) AUTO_INCREMENT=1;

INSERT INTO `myTable` (`name`,`phone`,`email`,`address`,`postalZip`,`region`,`country`,`alphanumeric`,`text`)
VALUES
  ("Malcolm Mcgee","1-831-777-4886","[email protected]","109-1617 Augue Av.","873766","Delta","Ukraine","HPJ88FSI6HJ","in faucibus orci luctus et ultrices posuere cubilia Curae Phasellus"),
  ("Harrison Dudley","(645) 987-7967","[email protected]","P.O. Box 311, 4823 Odio Street","81398-37524","Nordland","Germany","INL95CND6TF","egestas nunc sed libero. Proin sed turpis nec mauris blandit"),
  ("Driscoll Callahan","1-598-623-3631","[email protected]","689-226 Eu St.","4534 JV","California","Chile","QQI55BTP0CS","velit dui, semper et, lacinia vitae, sodales at, velit. Pellentesque"),
  ("Anne Rollins","(558) 943-1159","[email protected]","5361 Enim, Street","176814","Minas Gerais","Brazil","EVO25RST5RM","cursus vestibulum. Mauris magna. Duis dignissim tempor arcu. Vestibulum ut"),
  ("Noah Townsend","(514) 311-3416","[email protected]","797-7375 Consectetuer Ave","5177","Tây Ninh","Chile","CNG87EJF4EK","libero at auctor ullamcorper, nisl arcu iaculis enim, sit amet");

{
   "findings":[
      {
         "detector":{
            "id":"89f810aa-64a5-4269-b0a0-110d250d55ee",
            "name":"email address"
         },
         "finding":"[email protected]",
         "confidence":"LIKELY",
         "location":{
            "byteRange":{
               "start":36,
               "end":51
            },
            "codepointRange":{
               "start":36,
               "end":51
            },
            "lineRange":{
               "start":1,
               "end":1
            },
            "rowRange":{
               "start":1,
               "end":1
            },
            "columnRange":{
               "start":4,
               "end":4
            },
            "commitHash":""
         },
         "matchedDetectionRuleUUIDs":[
            "950833c9-8608-4c66-8a3a-0734eac11157"
         ],
         "matchedDetectionRules":[
            
         ]
      },

{
         "detector":{
            "id":"d08edfc4-b5e2-420a-a5fe-3693fb6276c4",
            "name":"Phone number",
            "version":1
         },
         "finding":"514) 311-3416",
         "confidence":"POSSIBLE",
         "location":{
            "byteRange":{
               "start":814,
               "end":827
            },
            "codepointRange":{
               "start":814,
               "end":827
            },
            "lineRange":{
               "start":5,
               "end":5
            },
            "rowRange":{
               "start":5,
               "end":5
            },
            "columnRange":{
               "start":3,
               "end":3
            },
            "commitHash":""
         },
         "matchedDetectionRuleUUIDs":[
            "950833c9-8608-4c66-8a3a-0734eac11157"
         ],
         "matchedDetectionRules":[
            
         ]
      }

git clone https://github.com/nightfallai/nightfall-kinesis-demo

pip install nightfall=1.2.0
pip install boto3

import os
import base64
import boto3
from nightfall import Nightfall


def lambda_handler(event, context):
    dynamodb = boto3.resource('dynamodb')
    table = dynamodb.Table('nightfall-findings')

    records = []
    for record in event['Records']:
        # Kinesis data is base64 encoded so decode here
        payload = base64.b64decode(record["kinesis"]["data"])
        records.append(payload.decode("utf-8"))

    nightfall = Nightfall(
        os.environ.get('NIGHTFALL_API_KEY')
    )

    findings, redactions = nightfall.scan_text(
        records,
        detection_rule_uuids=[os.environ.get('DETECTION_RULE_UUID')]
    )

    for record_i, record_findings in enumerate(findings):
        if record_findings:
            formatted_findings = []
            for finding in record_findings:
                formatted_findings.append({
                    'Finding': finding.finding,
                    'BeforeContext': finding.before_context,
                    'AfterContext': finding.after_context,
                    'DetectorName': finding.detector_name,
                    'DetectorUUID': finding.detector_uuid,
                    'ByteStart': finding.byte_range.start,
                    'ByteStop': finding.byte_range.stop,
                    'Confidence': finding.confidence.value,
                })

            table.put_item(
                Item={
                    'KinesisEventID': event['Records'][record_i]['eventID'],
                    'KinesisRecord': records[record_i],
                    'NightfallFindings': formatted_findings,
                }
            )

dynamodb = boto3.resource('dynamodb')
table = dynamodb.Table('nightfall-findings')

records = []
for record in event['Records']:
    # Kinesis data is base64 encoded so decode here
    payload = base64.b64decode(record["kinesis"]["data"])
    records.append(payload.decode("utf-8"))

nightfall = Nightfall(
    os.environ.get('NIGHTFALL_API_KEY')
)
    
findings, redactions = nightfall.scan_text(
    records,
    detection_rule_uuids=[os.environ.get('DETECTION_RULE_UUID')]
)

for record_i, record_findings in enumerate(findings):
    if record_findings:
        formatted_findings = []
        for finding in record_findings:
            formatted_findings.append({
                'Finding': finding.finding,
                'BeforeContext': finding.before_context,
                'AfterContext': finding.after_context,
                'DetectorName': finding.detector_name,
                'DetectorUUID': finding.detector_uuid,
                'ByteStart': finding.byte_range.start,
                'ByteStop': finding.byte_range.stop,
                'Confidence': finding.confidence.value,
            })

        table.put_item(
            Item={
                'KinesisEventID': event['Records'][record_i]['eventID'],
                'KinesisRecord': records[record_i],
                'NightfallFindings': formatted_findings,
           }
        )

'id': fake.uuid4(),
'name': fake.name(),
'email': fake.email(),
'message': fake.paragraph()

python3 -m venv venv
source venv/bin/activate

pip install -r requirements.txt

SENT TO KINESIS: {'id': '8a69f3f5-432e-4ec1-8295-e8b79236e36e', 'name': 'Jessica Henderson', 'email': '[email protected]', 'message': 'Eye evening ahead field. With energy all personal soon sense. Method decision TV that.'}
SENT TO KINESIS: {'id': 'd4a90b48-cbcd-45ca-a231-3edbbc0c4792', 'name': 'Thomas Cuevas', 'email': '[email protected]', 'message': 'People write from season. Upon drive before summer exactly tonight practice expert. Actually news reason particularly in should.'}
SENT TO KINESIS: {'id': '084083bc-114a-4cc5-8cd6-2e15fd26b6db', 'name': 'Nathan Ward', 'email': '[email protected]', 'message': 'Add school air visit physical range. Child that company late. Boy than remain. Early ability economy thought event option.'}