CAMP 11th Annual Meeting
Will AI Change Everything?
Predictions & recommendations for fighting internet abuse at scale
David Freeman | Principal Scientist, Delphi Research
Seoul, Korea · 7 July 2026
2
SETTING THE STAGE
What is “Cybersecurity”?
Wikipedia:
“Protecting software, systems, and networks from threats that lead to unauthorized disclosure, theft, or damage.”
My definition:
“Preventing people from using computers to cause harm.”
Financial loss
Denial of service
Data leakage
Physical harm
3
SETTING THE STAGE
What do I know about Cybersecurity?
3.5B
fake accounts disabled
Meta Transparency Report
5B
malicious ads blocked
Google Ads Safety Report
Millions
of advertisers verified
Meta / Google
Trillions
of scraping requests blocked
Meta Press
14 years working on high-volume adversarial problems:
Incidence will never go to zero.
The goal is to get the problem under control.
(2012-17) (2017-26)
Principles for Fighting Abuse at Scale
1. What gets measured gets fixed
2. Everything is a tradeoff
3. Change the economics
4. Focus on behavior
5
PRINCIPLE #1
What gets measured gets fixed
Example: Verifying advertisers
Which of these events should we count to size the problem?
These are all bad things. Only one reflects a bad user outcome.
Fake IDs accepted
Ads from unregistered businesses
Scam ads viewed
Advertiser location mismatches
Unverified advertiser actions
Scam ads viewed
6
PRINCIPLE #1, CONTINUED
Measure using multiple techniques
📣 User
reporting
✓ Reflects what users
care about
✗ Noisy, biased, can be
gamed
Best for: attack discovery
🏷️ Expert
labeling
✓ High quality,
statistically robust�✗ Expensive to scale
Best for: assessing interventions
📊 Proxy
metrics
✓ Easy to scale�✗ Loosely coupled with
the real goal
Best for: continuous monitoring
No single method is enough — monitor & set goals on as many as possible.
🎯 Red-� teaming
✓ Uncovers complex
vulnerabilities
✗ Doesn’t reflect attacker
behavior “in the wild”
Best for: proactive defense
7
PRINCIPLE #1, CONTINUED
Segment to find the low-hanging fruit
…breakdown after reducing unverified population
Solve one problem at a time. Focus on the segment with the biggest opportunity.
Example: Verifying advertisers
FICTIONAL breakdown of scam ads by advertiser segment:
initial breakdown…
8
PRINCIPLE #1, CONTINUED
Avoid common measurement mistakes
Takeaway: constantly re-measure, re-segment, re-prioritize.
9
PRINCIPLE #2
Everything is a tradeoff
High Security
Low Friction
✓ Not adversarially controlled
✗ Requires counterfactual assessment (holdout)
10
PRINCIPLE #3
Change the economics — Raise attack cost
Attackers are economically motivated.
Make the attack more expensive to run. For example:
Require login to access data.
Require verification to perform risky actions.
Adjust requirements based on probability of bad outcomes.
Go after financial chokepoints (see Kondrashin et al, McCoy et al)
Do all of the above (“defense in depth”)
11
PRINCIPLE #3, CONTINUED
Change the economics — Reduce attack value
Attackers are economically motivated.
If you can’t make the attack more expensive, make it pay off less per success.
Limit the number of actions a single account can perform.
Limit the amount of data returned per request.
Limit potential financial loss per user.
(& vary limits across segments)
12
PRINCIPLE #4
Don’t play “Whack-a-mole”
13
PRINCIPLE #4
Focus on behavior, not content
Attackers will always be anomalous in some dimension — the key is to
collect enough data to identify the anomaly.
Do less of these things: | Do more of these things: |
Invest in content classifiers to find violating instances. | Invest in clustering algorithms & cluster classification to find violating groups. |
Block specific IP addresses, app versions, text strings, etc. | Dynamically assess IP activity, app usage, content generation, etc. vs. benign baseline. |
Engage in an arms race to stop deep fakes. | Corroborate multiple identity sources against each other. |
14
PRINCIPLE #4, CONTINUED
Use information asymmetry in your favor
Now AI is Here!
What Changes?
1. What gets measured gets fixed?
2. Everything is a tradeoff?
3. Change the economics?
4. Focus on behavior?
16
HOW AI CHANGES THE GAME
Both sides get better at their jobs
Attackers can
Defenders can
Attacker’s job: Look like a benign user.
Defender’s job: Distinguish attack traffic from benign traffic.
But their jobs don’t change!
measure everything
malicious content
behavior analysis
economic motivation
17
HOW AI CHANGES THE GAME, CONTINUED
What AI doesn’t change
Attackers are still economically motivated.
Attackers never sleep!
Attack patterns still differ from normal behavior.
You still need objective, ground-truth observations to measure
opportunity and impact.
18
HOW AI CHANGES THE GAME, CONTINUED
A cautionary tale
You still need experts to determine whether the AI is correct!
(June 8 2026)
19
WRAPPING UP
Takeaways
1
Measure the bad user outcome — use many sources to get the complete picture.
2
Everything is a tradeoff — put numbers on both sides of the equation.
3
Shift the attacker’s cost/benefit calculation — raise cost or cut value.
4
Focus on behavior, not content — it’s expensive to fake consistently across all surfaces and dimensions.
AI increases speed and quality on both sides. It doesn’t change the fundamentals.
Thank You / 감사합니다
Questions?
David Freeman · www.delphiresear.ch
Opinions expressed in this talk are my own and do not represent the views of any current or past employer.