API Security Best Practices for 2026

The API security conversation changed shape in 2026, and not because anyone finally solved the old problems. Broken authentication and misconfiguration are still the two most common ways attackers get in, exactly like they were three years ago. What's different is who's calling the APIs now: a huge and rapidly growing share of traffic comes from AI agents, not human developers, and most security teams admit they can't actually tell the difference between a legitimate agent and a malicious one.

That's not a hypothetical risk. It's the central finding of Salt Security's most recent research, and it's worth sitting with before we get into the standard best-practices list, because it reframes why a lot of those practices matter more urgently now than they did even two years ago.

Where the threat actually stands right now

The scale of API-targeted attacks has grown substantially. The average number of API attacks per enterprise reached 258 per day in 2025, more than double the 121 per day recorded in 2024, according to Akamai's 2026 State of the Internet report. Across 2023 and 2024 combined, Akamai recorded 150 billion API attacks, and the company now states plainly that APIs have become the primary attack surface for most organizations, not a secondary concern behind web applications.

"Attackers increasingly focus on degrading performance, driving up infrastructure costs, and exploiting AI-driven automation at scale, rather than seeking headline-grabbing campaigns," said Patrick Sullivan, CTO of Security Strategy at Akamai. "Automation and AI are making these sophisticated campaigns cheap, repeatable, and fast. And as enterprises invest heavily in AI transformation, attackers are targeting the APIs that power that transformation."

Salt Security's 1H 2026 State of AI and API Security report, based on a survey of 327 security leaders, adds specific numbers to that shift. Two-thirds (66%) of organizations reported API growth of more than 50% in the past year, driven largely by automation and AI adoption. Nearly all (99%) of the attack attempts Salt Labs analyzed originated from authenticated sources, meaning attackers increasingly aren't breaking in at all, they're operating inside trusted systems using legitimate credentials, often through compromised or over-permissioned AI agents with no rate limiting and no behavioral guardrails applied to them.

"You cannot secure AI agents without securing every layer they touch, including the APIs they call, the MCP servers they route through, and the data they access," said Roey Eliyahu, co-founder and CEO of Salt Security. "Risk in the agentic era doesn't sit in one place. It lives in how all of those pieces interact in real time."

The visibility problem is the root of most of this

Nearly half of organizations, 48.9%, report being essentially blind to machine-to-machine traffic, unable to monitor what their own autonomous agents are actually doing, according to Salt Security's research. A similar share, 48.3%, say they can't reliably differentiate a legitimate AI agent from a malicious bot hitting the same endpoints. Only 24% have a fully automated API inventory; most organizations still rely on partial or manual tracking, which means a meaningful chunk of any given API footprint is effectively undocumented.

That visibility gap has real business consequences beyond the theoretical risk. Almost half of organizations, 47%, have delayed a production release specifically because of concerns about securing APIs exposed to AI systems. And despite 78.6% of boards now reporting increased executive scrutiny of AI security risk, only 23.5% of security leaders say their existing tools are "very effective" at preventing attacks. Legacy web application firewalls and basic API gateways were built around predictable human sessions and static signatures; they weren't designed to parse the unpredictable, logic-based behavior of an autonomous agent chaining together API calls at machine speed.

API security best practices, updated for where the threat actually is

The fundamentals below haven't changed in principle. What's changed is which ones matter most urgently, and why.

1. Build a real API inventory, not a partial one

Given that only 24% of organizations have a fully automated inventory, this is genuinely the starting point, not a checkbox. You cannot protect what you don't know exists, and shadow APIs, endpoints created by a team or an AI agent without security's knowledge, are exactly where attackers look first. Automated discovery tools that continuously scan for new and changed endpoints are no longer optional at any meaningful scale.

2. Treat authenticated traffic as a potential threat, not automatic trust

This is the practice that's shifted the most. With 99% of the attacks Salt Labs analyzed coming from authenticated sources, the old model, where you harden the perimeter and trust anything that gets past login, doesn't hold anymore. You need behavioral baselines that flag unusual patterns even from credentials that check out: a service account suddenly querying data it's never touched before, or an API key making requests at a volume or cadence that doesn't match its normal usage.

3. Fix security misconfiguration first

Almost two-thirds of attacks, 65%, exploit security misconfiguration (OWASP API8), per Salt Security's data, a vulnerability that gets dramatically worse when over-permissioned APIs are connected to AI agents capable of querying, chaining, and exfiltrating data at speed. This is unglamorous work, reviewing permission scopes, closing default-open endpoints, auditing who actually needs what access, but it's where the highest volume of real-world exploitation is happening right now, not exotic zero-days.

4. Apply strong encryption end to end

This one hasn't changed, and it shouldn't be skipped just because it's familiar advice. Use current TLS versions with mutual encryption for both internal and external API traffic. If data is intercepted, encryption is what determines whether it's useful to an attacker or just noise.

5. Minimize what any given API actually exposes

Strip confidential information, credentials, internal error details, IP addresses, before anything goes near a public-facing endpoint. Verbose error messages in particular are a gift to attackers doing reconnaissance; they'll happily use your own debugging output to map your system's weak points.

6. Set rate limits, and set them with AI traffic patterns in mind

Rate limiting has always mattered for stopping denial-of-service attempts, but the bar has moved. AI-driven attack tooling can generate requests at a volume and pace that human-scale rate limits weren't designed to catch. Review your thresholds against current traffic patterns rather than assuming limits set a few years ago still fit.

7. Validate every parameter, every time

Define explicit models for what constitutes a permissible input, and reject anything that falls outside them. This remains a genuinely effective way to block malformed or malicious requests before they reach application logic, and it matters even more when a growing share of requests originate from automated systems rather than predictable human input patterns.

8. Use layered API firewalls

A proper API firewall structure includes a demilitarized zone layer handling core checks, request size, HTTP-layer security, injection attempts, plus a second layer with deeper content inspection. Treating your API gateway as a single checkpoint rather than layered defense leaves gaps that a determined attacker, human or automated, will eventually find.

9. Run dynamic testing continuously, well past the initial launch

DAST tools testing REST, GraphQL, and SOAP architectures need to run on a continuous basis, not as a pre-launch gate that gets skipped once an API is live. APIs change constantly as features ship; testing that only happens once misses everything introduced afterward.

10. Reconsider identity and access models for non-human callers

OAuth and OpenID Connect remain solid for human-delegated authentication and authorization. But the growth in machine-to-machine and agent-to-API traffic means identity and access management now needs a parallel track built specifically for non-human callers, with its own credential lifecycle, its own anomaly detection, and its own audit trail. Bolting agent traffic onto a human-oriented IAM setup is where a lot of the current visibility gap comes from in the first place.

The honest tradeoff here

None of this is free, and it's worth saying plainly: securing APIs for an agentic AI environment costs more, in tooling, in engineering time, and in the friction of adding behavioral monitoring on top of systems that used to run on simpler rules. Almost half of organizations have already delayed shipping something because of these concerns. That's a real cost, and pretending otherwise doesn't help anyone budget correctly.

But the alternative cost is worse and better documented: 87% of organizations reported an API-related security incident in 2025, and the data shows that trend accelerating, not leveling off, as agentic AI adoption grows. The practical takeaway isn't "solve this perfectly before you ship anything AI-related." It's "treat API visibility and behavioral monitoring as a first-class requirement in your AI rollout plan, not an afterthought you'll get to once the feature is live." Teams that build the inventory and monitoring layer alongside the AI feature, rather than after an incident forces the issue, consistently end up spending less overall.

For the broader context on where API vulnerabilities originate, our deeper look at common API attack types and vulnerabilities pairs well with this piece, and if your organization is still building out foundational access controls, multi-factor authentication remains a baseline requirement, not a nice-to-have, for anything touching API-connected systems.