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Service MITRE
Secure the AI you use. Defend against the AI used on you.
AI moved into your business faster than your controls did — through the tools your staff already use, the software your vendors already ship, and the agents your developers already build. way11 secures both sides of that line.
The short version
Three things changed. All three are now measurable. You don't need an AI strategy to be exposed — you only need employees with a browser.
01
Attackers got faster
In November 2025, Anthropic disclosed the first documented large-scale AI-orchestrated espionage campaign — around 30 organizations targeted, with AI executing an estimated 80–90% of the tactical work and humans stepping in at only four to six decision points. It's now catalogued as MITRE ATT&CK Campaign C0062.
02
Your attack surface grew
Every model, prompt, dataset, API key and AI agent is a new door. Most organizations cannot say how many they have — and an AI agent is a privileged identity from the day it's switched on.
03
The rules arrived
CMMC became fully enforceable in February 2026. The FY2026 NDAA directs DoD to fold an AI/ML security framework into DFARS and CMMC. GovRAMP has added an AI overlay. NIST launched its AI Agent Standards Initiative.
Where we help
Nineteen areas. Start with one — most clients start with three. Each connects to the way11 services you may already use.
Where we help
01 AI Model Security
The risk
The models you build, fine-tune or host can be stolen, tampered with, or coaxed into revealing their training data. A model file is just a file — and it usually sits somewhere with far weaker controls than your database.
What way11 does
Inventory every model in use. Lock down registries, weights and artifacts. Add integrity checks so a swapped or poisoned model is caught before it serves a request. Version control that survives an audit.
02 AI Threat Detection
The risk
Attacks moving at machine speed produce signals your tooling was never tuned to see. An agent making 4,000 API calls at 3 a.m. looks like traffic, not like theft.
What way11 does
Build AI-specific detection content for your SIEM and SOC — prompt anomalies, token spikes, agent behaviour outside baseline, model endpoint abuse — mapped to MITRE ATT&CK so alerts land in a framework your team already reads.
03 AI in Security
The risk
Your team is outnumbered — alert volume is up, headcount is not. But bolting a chatbot onto a SOC creates noise and cost, not safety.
What way11 does
Deploy AI where it measurably reduces workload — triage, enrichment, first-pass investigation, reporting — with human sign-off on anything that changes state. Model-agnostic on purpose: security is a workflow, and you should keep the workflow when the model changes.
04 API Attacks
The risk
AI runs on APIs. Every model endpoint, agent tool call and integration is an API — and AI-assisted attackers enumerate and abuse them faster than humans ever could.
What way11 does
IDiscover shadow and undocumented APIs, including the ones your AI features created. Test authentication, authorisation and rate limits. Put an AI gateway in front of model endpoints so prompts, outputs and token spend are inspected and capped.
05 APT — Advanced Persistent Threats
The risk
AI runs on APIs. Every model endpoint, agent tool call and integration is an API — and AI-assisted attackers enumerate and abuse them faster than humans ever could.
What way11 does
Threat hunting built around your actual adversaries, not a generic list. Behavioural baselining for long-dwell activity. Compromise assessments when you need to know whether someone is already inside.
06 Autonomous Systems Security
The risk
An AI agent is a privileged identity from day one. It holds credentials, calls tools and acts on its own — and when it goes wrong, your logs often show a human’s name, not the agent’s.
What way11 does
Register every agent as a first-class identity. Least privilege and short-lived credentials that expire on their own. Runtime guardrails so an agent that drifts outside policy is stopped mid-task, not found in next quarter’s audit. Full action logging: which agent, on whose behalf, with what authority.
07 Data Poisoning
The risk
Corrupt the data going in and you corrupt every decision coming out — quietly, and often months before anyone notices. This covers training data, fine-tuning sets, and the document stores your retrieval systems read from.
What way11 does
Provenance and integrity controls on training and retrieval data. Access control on the knowledge bases your AI reads. Drift monitoring that flags when model behaviour shifts without a code change. Recovery planning — a poisoned model needs a clean rollback point.
08 Deepfake Detection
The risk
AI runs on APIs. Every model endpoint, agent tool call and integration is an API — and AI-assisted attackers enumerate and abuse them faster than humans ever could.
What way11 does
Deepfake-resistant verification for payments, payroll changes, vendor bank detail updates and remote hiring — out-of-band callbacks, liveness checks, and detection tooling where the risk justifies it. There is no single silver bullet, so we fix the process too: most successful deepfake fraud works because one approval step was missing.
09 GenAI Security
The risk
Shadow AI. Staff paste customer records, contracts and source code into consumer chatbots because it makes their day easier. Most organizations have this happening right now and cannot prove otherwise.a
What way11 does
What way11 does. Discover which AI tools are actually in use across your network and SaaS estate. An acceptable-use policy people will actually follow. Route usage through sanctioned, logged channels with data-loss controls on prompts and outputs. Give staff a good option so they stop using a bad one.
10 Machine Learning Bias
The risk
A model quietly weighs one goal against another — accuracy against fairness, fraud prevention against access — and never tells the people accountable for that trade-off. In lending, hiring, benefits eligibility and case triage, that is a legal exposure as much as a technical one.
What way11 does
Bias and fairness testing on models that touch people. Documentation of the trade-offs so an accountable human, not the model, owns the decision. Alignment to NIST AI RMF and ISO/IEC 42001 so the evidence exists before a regulator or auditor asks.
11 Nation-State Attacks
The risk
State actors no longer need a large team. They break an operation into small, innocuous-looking tasks, hand them to AI, and run reconnaissance, exploitation and exfiltration at scale. Municipalities, utilities, school districts and defense suppliers are squarely in scope — often as the soft route into someone larger.
What way11 does
Threat intelligence tuned to your sector and geography. Detection engineering against AI-orchestrated campaign patterns. Tabletop exercises for state and local scenarios. Coordination support with CISA, MS-ISAC and state fusion centers when something is live.
12 Phishing
The risk
The tells are gone. No broken grammar, no odd formatting — AI writes email in your CFO’s voice, referencing a real project, timed to a real deadline. “Train users to spot bad spelling” is no longer a control.
What way11 does
AI-generated phishing simulations reflecting what your people will actually receive, including voice and video lures. Email authentication hardening (SPF, DKIM, DMARC). Detection tuned for semantic manipulation rather than keyword matching. Reporting workflows that make it easy to raise a hand.
13 Prompt Injection Defense
The risk
Hidden instructions buried in a document, web page, calendar invite or email that your AI reads — and obeys. The danger concentrates where three things meet: the AI can see private data, it processes untrusted content, and it can send information out. Researchers call that the lethal trifecta.
What way11 does
Map where the trifecta exists in your AI systems and break it — usually by removing one leg rather than trying to filter every malicious prompt. Input and output inspection at the gateway. Tool-call allowlisting. Human confirmation on irreversible actions. Then we try to break it ourselves, before someone else does.
14 Ransomware
The risk
Faster reconnaissance, better-targeted extortion and AI-assisted negotiation pressure. The economics still favour attackers, and smaller organizations remain the preferred target because recovery capability is thinner.
What way11 does
The fundamentals, done properly and proven: segmentation, immutable and tested backups, privileged access controls, and EDR coverage with no gaps. Plus a response retainer so the first hour is executed, not improvised. We test restores — an untested backup is a hope, not a control.
15 SaaS Attacks
The risk
Your AI assistant is connected to your email, files, CRM and chat through OAuth grants nobody reviews. One over-permissioned integration and an attacker inherits everything that app can reach — without touching a password.
What way11 does
Full inventory of SaaS-to-SaaS and AI-to-SaaS connections. Review and revoke excessive OAuth scopes. Continuous posture monitoring for the platforms that matter — Microsoft 365, Google Workspace, Salesforce, and the AI tools bolted onto them. Detection for token theft and consent-grant phishing.
16 Social Engineering
The risk
Attackers now research your org chart, your vendors, your recent press release and your employees’ public posts — automatically — then build a pretext that fits. The help desk password reset is still the most reliable way in.
What way11 does
Test the human layer the way an attacker would: vishing, pretexting and help-desk impersonation. Harden identity verification for resets and privileged requests. Role-specific training for the people attackers actually target — finance, HR, IT support and executive assistants.
17 Supply Chain Attacks
The risk
You inherit the risk of every model, dataset, library, plugin and vendor you use. AI widened this considerably — a fine-tuned model from a public hub carries whatever went into it, and AI-generated code carries whatever the model learned.
What way11 does
Software and AI bills of materials (SBOM / AIBOM). Provenance checks on third-party models and datasets. Dependency and AI-generated code scanning inside your pipeline. Vendor due-diligence questions that actually surface AI risk — including whether your data trains their models.
18 Zero-Day Exploits
The risk
AI has become genuinely good at finding vulnerabilities in code. That cuts both ways — defenders are surfacing decades-old flaws, and so is everyone else. Expect a turbulent stretch as more of them come to light.
What way11 does
AI-assisted code review and vulnerability discovery in your own codebase, so you find it first. Ruthless patch prioritisation based on exploitability, not just CVSS score. Virtual patching and compensating controls when you cannot patch immediately. Continuous Vulnerability Management on a four-step cycle.
19 Token Torching (Emerging)
The risk
The attacker steals nothing and breaks nothing. They simply make your AI work absurdly hard — hiding decoy instructions or puzzles in content your AI consumes so it burns through tool calls and tokens. The answer comes back correct. The bill does not. Research published in 2026 demonstrated per-query cost inflation of up to 658×, with documented cases of five-figure damage inside three hours. Nothing looks wrong: valid inputs, valid outputs, no alert. OWASP tracks it as unbounded consumption.
What way11 does
Hard token and tool-call budgets per agent, per session, per user. Cost anomaly alerting wired into your SOC — not just your finance team’s monthly invoice. Loop and recursion limits. Sanitisation of content agents ingest from untrusted sources. And a simple economic baseline, so you know what normal costs before abnormal arrives.
01
AI Model Security
The risk
The models you build, fine-tune or host can be stolen, tampered with, or coaxed into revealing their training data. A model file is just a file — and it usually sits somewhere with far weaker controls than your database.
What way11 does
Inventory every model in use. Lock down registries, weights and artifacts. Add integrity checks so a swapped or poisoned model is caught before it serves a request. Version control that survives an audit.
02
AI Threat Detection
The risk
The models you build, fine-tune or host can be stolen, tampered with, or coaxed into revealing their training data. A model file is just a file — and it usually sits somewhere with far weaker controls than your database.
What way11 does
Inventory every model in use. Lock down registries, weights and artifacts. Add integrity checks so a swapped or poisoned model is caught before it serves a request. Version control that survives an audit.
03
AI in Security
The risk
The models you build, fine-tune or host can be stolen, tampered with, or coaxed into revealing their training data. A model file is just a file — and it usually sits somewhere with far weaker controls than your database.
What way11 does
Inventory every model in use. Lock down registries, weights and artifacts. Add integrity checks so a swapped or poisoned model is caught before it serves a request. Version control that survives an audit.
04
API Attacks
The risk
The models you build, fine-tune or host can be stolen, tampered with, or coaxed into revealing their training data. A model file is just a file — and it usually sits somewhere with far weaker controls than your database.
What way11 does
Inventory every model in use. Lock down registries, weights and artifacts. Add integrity checks so a swapped or poisoned model is caught before it serves a request. Version control that survives an audit.
05
APT — Advanced Persistent Threats
The risk
The models you build, fine-tune or host can be stolen, tampered with, or coaxed into revealing their training data. A model file is just a file — and it usually sits somewhere with far weaker controls than your database.
What way11 does
Inventory every model in use. Lock down registries, weights and artifacts. Add integrity checks so a swapped or poisoned model is caught before it serves a request. Version control that survives an audit.
06
Autonomous Systems Security
The risk
The models you build, fine-tune or host can be stolen, tampered with, or coaxed into revealing their training data. A model file is just a file — and it usually sits somewhere with far weaker controls than your database.
What way11 does
Inventory every model in use. Lock down registries, weights and artifacts. Add integrity checks so a swapped or poisoned model is caught before it serves a request. Version control that survives an audit.
07
Data Poisoning
The risk
The models you build, fine-tune or host can be stolen, tampered with, or coaxed into revealing their training data. A model file is just a file — and it usually sits somewhere with far weaker controls than your database.
What way11 does
Inventory every model in use. Lock down registries, weights and artifacts. Add integrity checks so a swapped or poisoned model is caught before it serves a request. Version control that survives an audit.
08
Deepfake Detection
The risk
The models you build, fine-tune or host can be stolen, tampered with, or coaxed into revealing their training data. A model file is just a file — and it usually sits somewhere with far weaker controls than your database.
What way11 does
Inventory every model in use. Lock down registries, weights and artifacts. Add integrity checks so a swapped or poisoned model is caught before it serves a request. Version control that survives an audit.
09
GenAI Security
The risk
The models you build, fine-tune or host can be stolen, tampered with, or coaxed into revealing their training data. A model file is just a file — and it usually sits somewhere with far weaker controls than your database.
What way11 does
Inventory every model in use. Lock down registries, weights and artifacts. Add integrity checks so a swapped or poisoned model is caught before it serves a request. Version control that survives an audit.
10
Machine Learning Bias
The risk
The models you build, fine-tune or host can be stolen, tampered with, or coaxed into revealing their training data. A model file is just a file — and it usually sits somewhere with far weaker controls than your database.
What way11 does
Inventory every model in use. Lock down registries, weights and artifacts. Add integrity checks so a swapped or poisoned model is caught before it serves a request. Version control that survives an audit.
11
Nation-State Attacks
The risk
The models you build, fine-tune or host can be stolen, tampered with, or coaxed into revealing their training data. A model file is just a file — and it usually sits somewhere with far weaker controls than your database.
What way11 does
Inventory every model in use. Lock down registries, weights and artifacts. Add integrity checks so a swapped or poisoned model is caught before it serves a request. Version control that survives an audit.
12
Phishing
The risk
The models you build, fine-tune or host can be stolen, tampered with, or coaxed into revealing their training data. A model file is just a file — and it usually sits somewhere with far weaker controls than your database.
What way11 does
Inventory every model in use. Lock down registries, weights and artifacts. Add integrity checks so a swapped or poisoned model is caught before it serves a request. Version control that survives an audit.
13
Prompt Injection Defense
The risk
The models you build, fine-tune or host can be stolen, tampered with, or coaxed into revealing their training data. A model file is just a file — and it usually sits somewhere with far weaker controls than your database.
What way11 does
Inventory every model in use. Lock down registries, weights and artifacts. Add integrity checks so a swapped or poisoned model is caught before it serves a request. Version control that survives an audit.
14
Ransomware
The risk
The models you build, fine-tune or host can be stolen, tampered with, or coaxed into revealing their training data. A model file is just a file — and it usually sits somewhere with far weaker controls than your database.
What way11 does
Inventory every model in use. Lock down registries, weights and artifacts. Add integrity checks so a swapped or poisoned model is caught before it serves a request. Version control that survives an audit.
15
SaaS Attacks
The risk
The models you build, fine-tune or host can be stolen, tampered with, or coaxed into revealing their training data. A model file is just a file — and it usually sits somewhere with far weaker controls than your database.
What way11 does
Inventory every model in use. Lock down registries, weights and artifacts. Add integrity checks so a swapped or poisoned model is caught before it serves a request. Version control that survives an audit.
16
Social Engineering
The risk
The models you build, fine-tune or host can be stolen, tampered with, or coaxed into revealing their training data. A model file is just a file — and it usually sits somewhere with far weaker controls than your database.
What way11 does
Inventory every model in use. Lock down registries, weights and artifacts. Add integrity checks so a swapped or poisoned model is caught before it serves a request. Version control that survives an audit.
17
Supply Chain Attacks
The risk
The models you build, fine-tune or host can be stolen, tampered with, or coaxed into revealing their training data. A model file is just a file — and it usually sits somewhere with far weaker controls than your database.
What way11 does
Inventory every model in use. Lock down registries, weights and artifacts. Add integrity checks so a swapped or poisoned model is caught before it serves a request. Version control that survives an audit.
18
Zero-Day Exploits
The risk
The models you build, fine-tune or host can be stolen, tampered with, or coaxed into revealing their training data. A model file is just a file — and it usually sits somewhere with far weaker controls than your database.
What way11 does
Inventory every model in use. Lock down registries, weights and artifacts. Add integrity checks so a swapped or poisoned model is caught before it serves a request. Version control that survives an audit.
19
Token Torching (Emerging)
The risk
The models you build, fine-tune or host can be stolen, tampered with, or coaxed into revealing their training data. A model file is just a file — and it usually sits somewhere with far weaker controls than your database.
What way11 does
Inventory every model in use. Lock down registries, weights and artifacts. Add integrity checks so a swapped or poisoned model is caught before it serves a request. Version control that survives an audit.
Packaged solutions
Built to sit alongside CIMAS, WASS, MASS and Continuous Vulnerability Management.
◆
AIMAS
AI Maturity Assessment Service
The starting point. A structured review of every place AI touches your business — tools, models, agents, data, vendors. A maturity score, ranked risk register, and a 90-day plan mapped to NIST AI RMF and ISO/IEC 42001.
Typical duration · 2–4 weeks
⨯
AIRT
AI Red Team
We attack your AI the way an adversary would: prompt injection, jailbreaks, data extraction, agent hijacking, tool misuse, token torching. Proof of what works, ranked by business impact, fixes attached.
Typical duration · 2–3 weeks / system
◍
AGIS
Agent & Identity Security
Bring every AI agent and non-human identity under control: registration, least privilege, short-lived credentials, runtime policy enforcement, end-to-end action logging.
Project, then optionally managed
◉
AI-SOC
AI-Aware Managed Detection
Detection content, monitoring and response for the AI layer — prompt anomalies, model endpoint abuse, agent behavioural drift, token-spend spikes, AI-orchestrated intrusion patterns.
Ongoing · monthly
Built for two buyers
Same discipline, different starting point.
Small & mid-sized business
You shouldn't need an AI governance committee
We start with the two things behind most SMB losses: someone pasting sensitive data into a chatbot, and someone approving a payment because a familiar voice asked them to. Fixed-scope engagements, plain-English reporting, controls that hold up without a full-time security team.
AIMAS Lite
(1 week)
→
GenAI acceptable use + monitoring
→
Deepfake-resistant payment verification
Federal, state, local & education
Compliance is the deadline. Sovereignty is the real question
Who controls your data, your models, and the layer they run in — we help you answer both. CMMC is fully enforceable as of Feb 2026, the FY2026 NDAA folds AI/ML into DFARS and CMMC, and GovRAMP now carries an AI overlay.
AIMAS
mapped to NIST AI RMF / CMMC
→
Agent identity governance
→
AI-aware detection & reporting
How engagements work
way11's 3D model — Develop, Decide, Deliver — applies to AI security the same way it applies to everything else.
STEP 01
Assess
A fixed-scope review that tells you where you actually stand — not where a vendor survey says you should be.
STEP 02
Remediate
A prioritised project to close what matters most, in order, with your team involved rather than sidelined.
STEP 03
Operate
Ongoing monitoring and response, run by us or alongside your team. No lock-in to a single AI vendor or model.
No prep needed
Start with a 30-minute AI Exposure Review.
We walk through where AI has entered your environment, what it can reach, and the three things worth fixing first. You leave with a written summary whether or not you engage us.
way11 Cybersecurity — Your Cyber Partner · 100+ certifications · 50+ technology partners
secure@way11.io · Frisco, TX 75035
Defending small & mid-sized businesses across the USA and U.S. territories. Eleven walls. One way through.
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