Елена — AI Integration & Security Lead
Moscow, Russia
Traditional security models assume human attackers: someone with a keyboard, limited time, and finite patience. They probe endpoints one at a time, test common vulnerabilities, and escalate when they find one. Security defenses evolved to counter this: rate limiting, WAFs, CAPTCHAs.
But the threat landscape has fundamentally changed. Modern attackers use AI agents that can probe an entire API surface in seconds, generate plausible-looking transactions, and adapt their attack patterns in real time based on the responses they receive. A CAPTCHA doesn't stop an AI. A rate limiter is easily circumvented by rotating IP addresses. Traditional security is fighting yesterday's war.
The Syrpts six-layer defense system was designed from the ground up to counter AI-driven attacks.
Most systems have a perimeter defense (firewall, rate limiter) and an application layer (input validation). If the attacker gets past the perimeter, the application is often unprotected. The Syrpts model uses defense in depth — six independent layers, each designed to catch what the previous layer missed.
Rate limiting (Layer 1) catches brute-force attacks. But a sophisticated attacker will stay just under the rate limit. That's where anomaly detection (Layer 2) takes over. It doesn't count requests — it analyzes patterns. A human user makes 3 requests, pauses, makes 2 more, checks the response, then continues. An AI makes exactly evenly-spaced requests with microsecond precision. The statistical signature is unmistakable.
This is my favorite layer. Honeypot endpoints are designed to look like vulnerabilities — an exposed admin panel, a debug state dump, an unsecured configuration endpoint. No legitimate user or software would ever access these URLs. Any request to a honeypot triggers an immediate IP ban and, critically, starts serving fake data with embedded tracking payloads.
The fake data is carefully crafted to look real but contain deliberate anomalies. If the attacker uses this data downstream — for example, to construct a forged transaction — the anomalies serve as a fingerprint that proves the data came from the honeypot. This creates a forensic trail even if the immediate attack is blocked.
If an attacker triggers multiple layers in sequence (e.g., rate limit → anomaly flag → honeypot hit), the system infers this is a coordinated reconnaissance operation. It automatically switches to read-only mode: all write endpoints return 503, all transactions are paused, and an alert is sent to the team. The lockdown persists until manual review confirms the threat is neutralized.
Even if all four outer layers fail, the cryptographic layer (key rotation, session invalidation) and the transaction validator (numeric integrity, signature verification) provide the final defense. These layers are not network-facing — they operate inside the application itself. An attacker would need to compromise the running process to bypass them.
The next evolution of the security system will incorporate machine learning models trained on attack patterns. The idea is simple: if attackers are using AI to find vulnerabilities, we should use AI to find the AI.
Key research areas:
After years of working in security, I've come to believe that the most important security tool is not a firewall, not encryption, not even AI. It's paranoia. Not the debilitating kind — the productive kind. The kind where every input is assumed malicious, every user is assumed compromised, and every system is assumed to be under active attack at all times.
This mindset leads to better code. Not because you're writing more security checks (though you are), but because you're designing systems that fail safely. When the inevitable breach occurs, a paranoid system limits the blast radius. A naive system amplifies it.