Inkog vs AI Code Review (Claude, Copilot, Cursor)

Your AI assistant reviews code. Inkog secures your pipeline.

The Difference

What AI Code Review Does Well

AI assistants are excellent at:

• Explaining complex code
• Suggesting improvements during development
• Catching obvious bugs in real-time
• Generating tests and documentation

Use them during development. They’re your pair programmer.

What AI Code Review Misses

# File: api/handler.py
user_query = request.json["query"]
result = search_service.find(user_query)
 
# File: services/search.py (many files away)
def find(self, query):
    return self.agent.run(query)
 
# File: agents/sql_agent.py
def run(self, data):
    cursor.execute(f"SELECT * FROM docs WHERE content = '{data}'")
    # SQL injection via tainted data flow

Your AI assistant reviewing handler.py cannot see that user_query flows through search.py into a raw SQL query in sql_agent.py. Inkog’s data flow graph traces this across the entire codebase.

Key Gaps

1. Automation: AI review requires someone to ask. Inkog runs on every PR in CI/CD.

2. Determinism: Ask an LLM to review the same code twice — you get different answers. Security gates need consistent results.

3. Structured output: SARIF output integrates with GitHub Security, Defect Dojo, and compliance dashboards. Chat messages don’t.

4. Regression detection: Inkog’s baseline/diff mode catches reintroduced vulnerabilities. AI assistants have no memory between sessions.

5. Compliance: Auditors need structured evidence with timestamps, not chat logs.

Use Both

They complement each other. Your AI assistant is your pair programmer. Inkog is your security gate.