Learning path

AI security professional

The full route for security teams: secure AI apps and agents, defend with AI, and govern it all.

39 lessons · 237 min left

Start path → From chatbot to agent

FoundationsLesson 3: From chatbot to agentA chatbot talks. An agent acts. That one difference is where the entire security field in this guide comes from.Beginner5 min read🧪 Lab📝 TestUp next
Agent securityLesson 7: Why the old security rules stop workingThirty years of security wisdom assumes code and data are separate things. Agents put them in the same pot and stir.Beginner5 min read🧪 Lab📝 Test
Agent securityLesson 8: The trust boundaryOne idea, drawn once, that the next fifteen modules all depend on. Where does trusted instruction end and untrusted content begin?Beginner6 min read🧪 Lab📝 Test
Agent securityLesson 9: Prompt injection, up closeThe number one risk on every serious list of AI vulnerabilities. Step through a live attack and then switch the defences on.Beginner6 min read🧪 Lab📝 Test
Agent securityLesson 11: The lethal trifectaThree capabilities that are each perfectly fine on their own. Put all three in one agent and you have built a data-theft machine. Flip the switches and watch.Middle6 min read🧪 Lab📝 Test
Securing AI applicationsLesson 135: The OWASP Top 10 for LLM applicationsA shared list of the ten biggest risks in apps built on large language models. Learn each one in a line, and where this course covers it.Middle7 min read🧪 Lab📝 Test
Securing AI applicationsLesson 136: Threat modelling an AI featureBefore you build, draw how data flows, mark where trust changes, and ask what could go wrong. Here is a practical method for AI features.Middle8 min read🧪 Lab📝 Test
Securing AI applicationsLesson 137: Treat model output as untrusted inputModel output is partly written by whoever wrote the text the model read. Escape it, validate it and allow-list it before any other code uses it.Middle8 min read🧪 Lab📝 Test
Securing AI applicationsLesson 138: System prompt leakage: prompts are not secretsAssume your system prompt will be read by users. Keep keys and security rules out of it, and enforce what matters in code.Middle6 min read🧪 Lab📝 Test
Securing AI applicationsLesson 139: Securing RAG: permissions for retrieved documentsRetrieval decides what the model sees. If retrieval ignores who the user is, the model will happily answer from documents they should never read.Middle6 min read🧪 Lab📝 Test
Securing AI applicationsLesson 140: Vector stores and embedding leaksEmbeddings look like harmless lists of numbers. Treat them, and the vector store that holds them, as carefully as the original text.Advanced6 min read🧪 Lab📝 Test
Securing AI applicationsLesson 141: Unbounded consumption: denial of walletEvery model call costs money. Without limits, one attacker, one bot or one bug in a loop can burn your budget or take the feature down for everyone.Middle6 min read🧪 Lab📝 Test
Securing AI applicationsLesson 142: Misinformation and overreliance in AI featuresModels can be confidently wrong. The danger grows when your product makes wrong answers look trustworthy, or when people and systems act on them without checking.Middle5 min read🧪 Lab📝 Test
Securing AI applicationsLesson 143: AI red teaming: methods and toolsRed teaming is a planned attack on your own AI system to find weaknesses before others do. Learn how to scope it, which tools help, and how to measure and report results.Middle6 min read🧪 Lab📝 Test
Securing AI applicationsLesson 144: Security testing in the AI pipelineRed teaming finds problems once. Your build pipeline keeps them fixed. Turn findings into tests, scan what you depend on, and re-test whenever the model changes.Advanced6 min read🧪 Lab📝 Test
Securing AI applicationsLesson 145: Logging and monitoring AI apps in productionYou cannot investigate what you did not record. Log the right things about every AI interaction, protect the personal data inside those logs, and watch for abuse.Middle8 min read🧪 Lab📝 Test
Agentic security in the enterpriseLesson 146: Shadow AI and the agent inventoryPeople in your company already use AI tools and agents you never approved. You cannot secure what you cannot see, so the first job is to find them and write them down.Middle7 min read🧪 Lab📝 Test
Agentic security in the enterpriseLesson 147: Agent identity: non-human identities and least privilegeEvery agent should log in as itself, with short-lived credentials and only the permissions its job needs. Shared human accounts and forever-keys turn every mistake into a mystery.Advanced5 min read🧪 Lab📝 Test
Agentic security in the enterpriseLesson 148: Delegated access: acting on behalf of usersWhen an agent works for a person, it should carry that person's permissions, narrowed further, never widened. OAuth scopes, consent and audit make that real.Advanced6 min read🧪 Lab📝 Test
Agentic security in the enterpriseLesson 149: Hardening MCP servers and clientsThe Model Context Protocol makes it easy to plug tools into agents. That ease is the risk. Here is how to run MCP in a company without handing out your data.Advanced7 min read🧪 Lab📝 Test
Agentic security in the enterpriseLesson 150: Tool poisoning and rug pullsA tool's description is read by the model as instructions. Attackers hide orders there, or change a trusted tool after you approved it. Here is how to catch both.Advanced6 min read🧪 Lab📝 Test
Agentic security in the enterpriseLesson 151: Agent-to-agent protocols and trustAgents from different teams and companies are starting to talk to each other directly. Every message from another agent is untrusted input, however polite it looks.Advanced5 min read🧪 Lab📝 Test
Agentic security in the enterpriseLesson 152: Browser and computer-use agentsAgents that click, type and scroll like a person can reach anything a logged-in browser can. Every page they read is a possible attacker.Advanced6 min read🧪 Lab📝 Test
Agentic security in the enterpriseLesson 153: Coding agents: secrets, packages and pull requestsCoding agents read your code, run commands and open pull requests. They meet untrusted text in issues and dependencies, and they can reach your secrets. Here is how to use them safely.Advanced6 min read🧪 Lab📝 Test
Agentic security in the enterpriseLesson 154: Agents in business workflows: payments, email and ticketsAgents in finance, HR and support desks touch money and people's data. Old business controls, like maker-checker and approval limits, work well here if you apply them to agents too.Middle6 min read🧪 Lab📝 Test
Agentic security in the enterpriseLesson 155: Runtime security for agents: policies and gatewaysRules written in a prompt can be talked around. Rules enforced by a gateway between the agent and its tools cannot. Here is how to build that layer.Advanced7 min read🧪 Lab📝 Test
Agentic security in the enterpriseLesson 156: Incident response for agentic systemsThe earlier lesson covered the first ten minutes. This one turns it into a company playbook: roles, steps, evidence, clean-up and a tabletop exercise to practise it.Advanced6 min read🧪 Lab📝 Test
Agentic security in the enterpriseLesson 157: Governing agents: approvals, audits and accountabilitySecurity controls need a process around them: who approves an agent, how risky it is, who owns it, and how you check it is still safe months later.Middle6 min read🧪 Lab📝 Test
Model and data securityLesson 169: Adversarial examples: fooling a modelA tiny, carefully chosen change to an input can flip a model's answer. Learn why this happens, where it matters, and what defences really achieve.Middle7 min read🧪 Lab📝 Test
Model and data securityLesson 170: Data poisoning and backdoorsIf attackers can change what a model learns from, they can change how it behaves. Learn how poisoning and hidden triggers work, and how to guard your data.Middle5 min read🧪 Lab📝 Test
Model and data securityLesson 171: Stealing models and training dataA model behind an API can leak itself and the data it learned from. Learn about model extraction, membership inference and memorisation, and the controls that reduce them.Advanced6 min read🧪 Lab📝 Test
Model and data securityLesson 172: Model files that run code: pickle and safetensorsSome model file formats can run code the moment you load them. Learn why, and how to load models safely.Middle5 min read🧪 Lab📝 Test
Model and data securityLesson 173: Provenance: signing models and the AI bill of materialsYou cannot trust a model you cannot trace. Learn hashes, signing, build provenance and the AI bill of materials.Advanced7 min read🧪 Lab📝 Test
Model and data securityLesson 174: Securing the training and fine-tuning pipelineThe pipeline that builds your model is a high-value target. Learn to lock down data, compute, secrets, checkpoints and releases.Advanced5 min read🧪 Lab📝 Test
Model and data securityLesson 175: Privacy-preserving machine learningDifferential privacy, federated learning, anonymisation and synthetic data: what each really protects, and what it costs.Advanced5 min read🧪 Lab📝 Test
Model and data securityLesson 176: Personal data in prompts, logs and training dataPersonal data leaks into AI systems through prompts, logs and datasets. Learn to minimise it, redact it, limit how long you keep it, and respect people's rights.Middle6 min read🧪 Lab📝 Test
Model and data securityLesson 177: AI risk frameworks: NIST AI RMF and ISO/IEC 42001Two widely used frameworks help you organise AI risk work and prove it to others. Learn what they are and how security teams use them.Advanced5 min read🧪 Lab📝 Test
Model and data securityLesson 178: The EU AI Act and India's rules for security teamsWhat the EU AI Act and India's data and cyber rules mean for teams that secure AI, in general terms. Always confirm current rules with legal counsel.Advanced6 min read🧪 Lab📝 Test
Model and data securityLesson 179: Running an AI security programmePut it all together: inventory, risk tiers, policies, secure development, testing, monitoring, response and reporting, plus a 90-day plan to start.Advanced7 min read🧪 Lab📝 Test