my prediction: "2025 is the year of ai agents but 2026 will be the year of ai security."
we're almost half way through 2025 and ai agents are already in production!
the next natural evolution is security threats and reports of hacks; because hackers love exploiting productionized products especially new and innovative ones.
most of the cybersecurity companies are already positioning themselves as ai security providers. consolidation has started and will continue for the rest of this year.
palo alto networks just acquired protect ai for over $500 million, highlighting how crucial ai security has become; if it wasn't already.
the global ai cybersecurity market is expanding dramatically—from $25 billion in 2023 to a projected $135 billion by 2030. startups and established cybersecurity firms alike are attracting significant investment, positioning themselves as essential providers of ai security solutions.
llm's have been known to have security vulnerabilities, and ai agents are going to be no exception. in fact, ai agents just magnify the risk of these vulnerabilities.
unique threats in ai agent security
ai agents introduce specific vulnerabilities beyond traditional cybersecurity risks:
- data poisoning: attackers deliberately corrupt ai training datasets, resulting in incorrect or malicious outcomes.
- prompt injection: adversaries manipulate ai inputs to bypass security controls and cause unintended disclosures.
- model theft: proprietary ai models and critical business data risk being stolen, potentially leading to severe competitive disadvantages.
- tool misuse: attackers can manipulate ai agents to misuse their integrated tools, potentially triggering harmful actions or exploiting vulnerabilities.
- credential leakage: exposed service tokens or secrets can lead to impersonation, privilege escalation, or infrastructure compromise.
- unauthorized code execution: unsecured code interpreters in ai agents can expose systems to arbitrary code execution and unauthorized access.
these are not hypothetical risks. high-profile incidents, including microsoft's bing chat revealing sensitive internal rules and financial frauds involving deepfake technologies, have demonstrated the real-world impact of these threats.
the need for secure ai agents
as ai agents become more autonomous and powerful, they require specialized security approaches which involve addressing multiple layers:
- the foundation model itself
- the agent framework
- the tools and integrations
- the runtime environment
- the data being processed
each layer presents unique challenges and requires specific security controls. in a future blog post, i'll dive into practical implementation strategies for securing crewai agents, including code examples.
ai agent security startups worth watching
- founded: 2019
- funding: $50m series a (2023)
- unique approach: ml security platform with automated threat detection
protect ai (acquired by palo alto networks in 2025)
- founded: 2022
- funding: $108.5m
- unique approach: secures ml supply chains and devsecops integration
robust intelligence (acquired by cisco in 2024)
- founded: 2019
- funding: $53m
- unique approach: ai firewall and proactive model validation
- founded: 2021
- funding: $20m series a (2024)
- unique approach: real-time security for generative ai applications
- founded: 2018
- funding: $23m series a1
- unique approach: validates ai model safety and continuous monitoring
- founded: 2019
- funding: $8m
- unique approach: focuses on adversarial robustness and protection against ml model attacks
- founded: 2020
- funding: $123m (series b in 2022)
- unique approach: passwordless authentication platform enhancing security for ai applications
note: this list is not exhaustive. am sure i missed some. feel free to reach out to me if you wish to add any.