API Gateway

API Security and Runtime Protection: A Practical Deep Dive into Salt Security, Traceable AI, Akamai, and Levo.ai for DevOps Engineers

Opening: Why Your APIs Are Open Season for Cybercriminals

Over 40,000 API security incidents in the first half of 2025 alone. Pause for a moment—how many attacks is that per day? Roughly 220 relentless strikes targeting the very lifeblood of your applications. And if your security confidence rests on your API gateway’s firewall or a last-minute security scan, brace yourself for a bitter dose of reality. Legacy defence mechanisms choke on the sprawling complexity and blistering agility of today’s API ecosystems, leaving security teams to wade through a tsunami of false positives and gaping blind spots. Runtime protection isn’t just a fancy luxury anymore—it’s the thin line between “everything’s fine” and a total, catastrophic data breach. Salt Security’s 2025 State of API Security report puts this into sharp focus.

1. Introduction: The API Security Crisis DevOps Can No Longer Ignore

APIs aren’t just tools anymore—they’re sprawling digital arteries pumping data across mobile apps, cloud services, microservices, and partner ecosystems. This explosive growth has simultaneously detonated your attack surface. Forget perimeter defence; attackers now exploit wherever APIs communicate, manipulating services, chaining calls, and abusing business logic with AI precision that would’ve blown hackers’ minds just years ago.

Traditional defence tactics—pre-production scans, static analysis, and perimeter firewalls—are simply no longer fit for purpose. They miss the cunning runtime attacks that mutate by the minute and bombard your team with noisy, useless alerts. I’ve lived that chaos more times than I care to admit. It’s time to shift from static scanning to continuous, AI-driven runtime protection, or risk being caught flat-footed.

2. Understanding the Threat Landscape: What We’re Up Against

AI-Agent-Driven API Misuse

Hackers deploy AI bots that mimic legitimate users to probe your APIs relentlessly, changing behaviour, timing, and input formats to evade hokey old detection methods. They exploit authentication gaps, input validation flaws, and cunning business logic vulnerabilities with unnerving finesse. As spotlighted by Salt Security’s recent launch addressing AI agent risks, this threat vector is evolving fast.

Shadow, Zombie, and Partner APIs: The Invisible Killers

Here’s a little secret: your official API inventory is a fairy tale. Shadow APIs lurk—undocumented, forgotten endpoints running amok. Zombie APIs, long abandoned but still alive and kicking. Uncontrolled partner APIs sneak in unmanaged, creating a dense fog where attackers love to hide. No monitoring, no alerts, just open invitation. Traceable AI excels at hunting these down via deep telemetry married to source-code analysis.

Logic-Based Chained Attacks

Forget blunt-force assaults. Modern attacks subtly chain API calls to exploit orchestration glitches, escalate privileges, or sidestep authorisation. Without real-time behavioural tracing, these ghostly manoeuvres slip right under the radar.

Runtime Evasion

Attackers use encryption, polymorphic payloads, and unpredictable request patterns that static signatures and firewalls simply cannot keep up with. These runtime subtleties evaporate from conventional scanners’ field of vision, creating blind spots as wide as Wales.

3. Why Runtime Protection and AI-Powered Continuous Discovery Matter

The game changer? Moving from point-in-time scans to a symphony of continuous behavioural intelligence. AI-driven platforms analyse real API traffic, build behavioural baselines, and spot anomalies in live operation—unmasking threats static tools fail to see. This approach reduces the scream-fest of false positives, offering context-rich alerts that actually mean something.

Best of all, these platforms mesh seamlessly with DevOps pipelines, no harmful drag on your CI/CD velocity allowed. They continuously discover all APIs—including those pesky shadows and zombies—keeping your inventory alive and kicking. The result? A dynamic, laser-focused security posture that adapts as fast as attackers try new tricks.

4. Deep Technical Exploration of Leading Platforms

4.1 Salt Security: AI-Infused Runtime Intelligence

Salt Security operates a passive sensor monitoring API traffic in real time, piping data to an AI engine that builds finely nuanced behavioural models. It separates friend from foe, catching AI-powered abuse and devious call chains others miss.

You’ll find Salt versatile, deployable inline or out-of-band, integrating with API gateways or load balancers without a noticeable performance hit. Recent updates focus on securing AI-agent actions specifically.

Deployment tip: I always start with read-only mode to map normal patterns before setting enforcement policies. This move cuts false positives and avoids “panic mode” on day one.

Official Salt Security resources

4.2 Traceable AI: Continuous API Discovery and Posture Insights

Traceable AI excels at hunting down shadow and zombie APIs through deep runtime telemetry married to source-code analysis. It assigns risk scores that highlight sensitive or critical endpoints.

Traceable’s runtime protection leans on RASP (Runtime Application Self-Protection) techniques embedded in your app code, blocking attacks in real time.

Implementation nugget: Embedding Traceable’s agents or SDKs with your developers takes coordination and timing to dodge performance slowdowns. But trust me: once set up, it’s a formidable line of defence.

Traceable AI documentation (please verify the precise link as vendor sites update frequently)

4.3 Akamai API Security: Machine Learning on the Edge

Akamai deploys its machine learning-enhanced API protection across a global edge network, stopping threats at the very borders of your reach. It detects over 200 attack methods with near-instant response, combining firewall, bot mitigation, and anomaly detection into one sleek box of tricks.

Operational note: The edge-first approach demands meticulous initial tuning to match your ever-evolving API versions. Miss this step, and you’ll drown in false alarms. But when it’s right, Akamai’s low-latency global coverage is a formidable shield.

Akamai API Security Overview

4.4 Levo.ai: AI-Powered Continuous Monitoring and Automated Remediation

Levo.ai specialises in continuous runtime monitoring to expose shadow, zombie, and partner APIs, tagging them with metadata that feeds risk-aware automated workflows. Think automatic access revocations and notifications sent straight to your dev teams.

It plugs directly into your DevOps pipelines, pushing proactive security forward.

Challenge: The floods of data generated can drown operators if you lack solid alert prioritisation and seamless incident management integration. Blink here, and your defenders might drown in alerts.

Levo.ai Runtime Protection Guide

4.5 Bonus: Contrast Security and Hdiv Runtime Protection

Contrast One embeds runtime protection via instrumentation inside your apps to block attacks live. Hdiv focuses on Java and .NET environments offering comparable RASP capabilities. Both platforms offer niche solutions worth investigating for targeted needs.

Contrast Security Runtime Protection

5. Practical Implementation Patterns and Integration Strategies

Integrate Runtime Visibility Early: Start monitoring immediately in non-blocking mode to capture normal API traffic before enabling enforcement. Agents or API gateway hooks are your friends here.
Shift-Left with API Security in CI/CD: Combine static and dynamic API scanning pre-deployment to weed out vulnerabilities early. Automate security gates that halt risky builds. Supplement with modern testing techniques as detailed in Next-Generation Application Testing: Mastering Invicti, Bright Security DAST, Beagle Security, and AI-Powered Scanning.
Automate Posture Improvement: Use continuous feedback loops from runtime insights to prioritise fixes. Push risk and vulnerability data back to dev teams to shrink your attack surface over time.
Automate Incident Response: Build playbooks that react automatically to alerts; throttle suspicious IPs, dial down risky shadow API endpoints, escalate forensic investigations where needed.
Balance Alert Noise: Tune AI thresholds, combine signals with observability data to reduce false positives. To ease triage, insights from AI-Powered Code Analysis: Transforming DevOps with AWS CodeGuru, GitHub Copilot, Amazon Q Developer, and Snyk AI Security are invaluable.

6. The Aha Moment: Rethinking API Security Beyond Static Scan Paradigms

The old-school “scan-and-pray” approach is officially dead. AI-powered runtime intelligence turns security from reactive firefighting into continuous, proactive posture management.

For DevOps engineers like us, this means fewer 3AM crisis calls, faster alert triage, and developers who actually trust security teams—because runtime protection finally works as promised. It’s no longer a luxury; it’s a necessity.

7. Forward-Looking Innovation and Emerging Trends

Behavioural Threat Hunting Evolves: Next-gen AI autonomously uncovers unknown attack signatures by analysing complex API behaviours and call sequences.
Autonomous Posture Management: Self-tuning policies that ramp up defences automatically, adapting to evolving threat environments without constant human intervention.
API Ecosystem Coverage: Expanding runtime protection beyond your APIs to include third-party and partner integrations — the usual weakest links.
Zero-Trust API Architecture: Runtime protection married with zero-trust models eliminates any implicit trust in API calls.
Explainable AI: Shiny, transparent alerts that tell you exactly why an API request tripped the alarm, slashing investigation times.
Integration with Application Security Orchestration: Runtime protection becomes a key player in holistic security pipelines and automated response workflows.

8. Conclusion: Concrete Next Steps for DevOps Teams

Prioritise scalable AI-powered runtime visibility platforms for your APIs to uncover all threats always.
Begin deployments in non-blocking modes to build team confidence and understand your normal traffic patterns.
Automate continuous feedback loops to nudge developers towards high-impact fixes.
Define operational metrics— track alert noise ratios, average time to detect/respond, and how well shadow APIs are covered.
Commit to learning and evolving with emerging technologies, fostering a culture of continuous security improvement.

Remember, in API security warfare, it’s not if you’ll be targeted—it’s when. Prepare accordingly, and make your runtime protection a steadfast ally.

Production-Ready Code Snippet: Embedding an API Runtime Agent (Hypothetical Example)

# Initialising a runtime protection agent for a Python Flask app
from runtime_protection_agent import RuntimeAgent
from flask import Flask, request

app = Flask(__name__)
# Initialise RuntimeAgent with your valid API key
agent = RuntimeAgent(api_key="YOUR_API_KEY")

@app.before_request
def before_request():
    try:
        # Inspect incoming request through runtime protection agent
        if not agent.inspect_request(request):
            # Block suspicious requests with 403 Forbidden response
            return "Request blocked by runtime protection", 403
    except Exception as e:
        # Failsafe: if agent crashes or errors, allow request but log the issue
        app.logger.error(f'Runtime protection error: {e}')
    # Continue normal processing otherwise
    return None

@app.after_request
def after_request(response):
    try:
        # Log response details for contextual analysis
        agent.log_response(response)
    except Exception as e:
        # Log errors but do not disrupt response flow
        app.logger.error(f'Runtime agent logging error: {e}')
    return response

# Example route with basic error handling
@app.route('/user/<id>')
def get_user(id):
    try:
        user_data = get_user_data(id)  # Assume secure function to fetch user info
        if user_data:
            # Return user data with 200 OK
            return user_data
        else:
            # 404 if user not found
            return "User not found", 404
    except Exception as ex:
        # Log unexpected errors and return 500 Internal Server Error
        app.logger.error(f'Error fetching user data for ID {id}: {ex}')
        return "Internal server error", 500

# Note: Adapt implementation according to chosen vendor’s SDK best practices.

References

This war story from the trenches of API security is your gritty tactical edge. Run lean, keep vigilant eyes on runtime, and treat your APIs like the crown jewels they are. Because when the next attack hits—and it will—you’ll want a runtime shield that actually works.

Written with scars and tough love by your battle-hardened DevOps engineer.

Next-Generation Application Testing: Mastering Invicti, Bright Security DAST, Beagle Security, and AI-Powered Scanning for Real-World DevOps Security

AI-Powered Code Analysis: Transforming DevOps with AWS CodeGuru, GitHub Copilot, Amazon Q Developer, and Snyk AI Security

Cloud Security and Container Protection Unlocked: Hands-On with Prisma Cloud, Aqua Security, Lasso Security, and AI Governance Platforms