Phishing used to be the easy alert. Bad grammar. A link to “Amazon.com.” A sender address off by one character. Analysts joked about it. You almost felt bad for the attacker.
That era is over. AI killed it.
Today’s phishing email is grammatically perfect, personalized with details scraped from LinkedIn, timed to land right after a real invoice was due, and sometimes followed up by a cloned voice on a “callback to verify.” The red flags we trained a generation of employees to spot, such as typos, generic greetings, and weird formatting, are gone, because the attacker is using the same language models you are.
Prevention was never going to win this one
Security awareness training still matters. Email filtering still matters. But let’s be honest about the math: attackers only need one click, and AI just made that click a lot more likely. The old plan to stop 100% of phishing at the gateway was never realistic, and it’s less realistic every quarter.
Rather than asking, “How do we stop every phishing email?” We need to figure out how fast we can find out what actually happened when someone clicks.
That’s an investigation problem, not a prevention problem, and it lives or dies on whether you have the right logs in the right place before the incident starts.
The logs that actually matter
Not all of these will apply to every environment, but if you’re missing more than one, your phishing investigations are running on guesswork.
- Email gateway and mail flow logs. Headers, SPF/DKIM/DMARC results, attachment and URL rewrite verdicts, and delivery status. This is where you confirm what the user actually received, not what the security awareness training screenshot claims they received. Integrations from tools like Abnormal Security, Proofpoint, and Gmail trace logs all feed this.
- Identity and authentication logs. MFA prompts approved or denied, new device sign-ins, impossible travel and conditional access decisions. If the phishing campaign was credential harvesting, this is where you find out whether it worked.
- EDR and process execution logs. These show what ran after the click. Look for macro execution, spawned child processes, and anything reaching out to a domain that didn’t exist a week ago.
- DNS and proxy logs. Did the user’s machine actually resolve or connect to the malicious domain, or did the click get intercepted upstream? Newly registered domains are still one of the best signals you have.
- SaaS and mailbox audit logs. OAuth consent grants and mailbox rule changes are the quiet part of business email compromise. An attacker who gets in doesn’t need malware. They just need a forwarding rule nobody’s watching.
Individually, each of these tells you a fragment. A mail flow log tells you a phish was delivered. An identity log tells you someone logged in from a new location three minutes later. Neither tells you the story. Together, they do.
Five tabs is not an investigation
This is the part nobody wants to admit: most phishing investigations aren’t slow because the logs don’t exist. They’re slow because the logs are spread across five different consoles, and your analyst is stitching them together by hand under pressure while the CISO asks for an update.
That’s the actual bottleneck. Not visibility. Correlation speed.
This is exactly what Cloud SIEM is built to do. It pulls email, identity, EDR, DNS, and SaaS audit data into one timeline instead of five browser tabs. When an Insight fires, the Summary Agent explains what triggered it in plain language instead of a wall of raw fields. From there, the SOC Analyst Agent can autonomously investigate, correlate it against threat intelligence, map it to MITRE ATT&CK, and hand your analyst a severity verdict with the evidence trail already assembled, not a black box saying “high severity, trust me.”
If the automated pass isn’t enough, and your analyst needs to ask a specific question, like “did this mailbox have any forwarding rules added in the last 30 days,” that’s where the new Mobot comes in. You can ask it a multi-step question, and it figures out which data sources to pull, what time range makes sense, and reasons across the results instead of making your analyst manually pivot between five queries.
Final word
Phishing didn’t get harder to stop because attackers got smarter. It got harder to stop because they got access to the same tools you did, and now they’re just faster than your old process. You’re not going to out-filter an LLM. You can out-investigate one, but only if the logs are already correlated before the first click ever lands.
Curious how fast your team could actually answer “what happened after the click?” Get a demo of Sumo Logic Cloud SIEM.
